Showing papers on "Content-based image retrieval published in 1996"
TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
Abstract: Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.
4,017 citations
TL;DR: This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection, and demonstrates the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects.
Abstract: This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects presented as "well-framed" views, and compare it with that of the principal component analysis.
1,713 citations
TL;DR: The Virage engine provides an open framework for developers to 'plug-in' primitives to solve specific image management problems and can be utilized to address high-level problems as well, such as automatic, unsupervised keyword assignment, or image classification.
Abstract: Until recently, the management of large image databases has relied exclusively on manually entered alphanumeric annotations. Systems are beginning to emerge in both the research and commercial sectors based on 'content-based' image retrieval, a technique which explicitly manages image assets by directly representing their visual attributes. The Virage image search engine provides an open framework for building such systems. The Virage engine expresses visual features as image 'primitives.' Primitives can be very general (such as color, shape, or texture) or quite domain specific (face recognition, cancer cell detection, etc.). The basic philosophy underlying this architecture is a transformation from the data-rich representation of explicit image pixels to a compact, semantic-rich representation of visually salient characteristics. In practice, the design of such primitives is non-trivial, and is driven by a number of conflicting real-world constraints (e.g. computation time vs. accuracy). The virage engine provides an open framework for developers to 'plug-in' primitives to solve specific image management problems. The architecture has been designed to support both static images and video in a unified paradigm. The infrastructure provided by the Virage engine can be utilized to address high-level problems as well, such as automatic, unsupervised keyword assignment, or image classification.
921 citations
TL;DR: The Wold model appears to offer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria.
Abstract: One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as database retrieval, it is important that image features used in pattern comparison provide good measures of image perceptual similarities. We present an image model with a new set of features that address the challenge of perceptual similarity. The model is based on the 2D Wold decomposition of homogeneous random fields. The three resulting mutually orthogonal subfields have perceptual properties which can be described as "periodicity," "directionality," and "randomness," approximating what are indicated to be the three most important dimensions of human texture perception. The method presented improves upon earlier Wold-based models in its tolerance to a variety of local inhomogeneities which arise in natural textures and its invariance under image transformation such as rotation. An image retrieval algorithm based on the new texture model is presented. Different types of image features are aggregated for similarity comparison by using a Bayesian probabilistic approach. The, effectiveness of the Wold model at retrieving perceptually similar natural textures is demonstrated in comparison to that of two other well-known pattern recognition methods. The Wold model appears to offer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria. Examples of natural scene Wold texture modeling are also presented.
618 citations
18 Jun 1996
TL;DR: A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database, and these features compare favorably with other existing texture representations.
Abstract: This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. With learning similarity the performance of similar pattern retrieval improves significantly. An important aspect of this work is its application to real image data. Texture feature extraction with similarity learning is used to search through large aerial photographs. Feature clustering enables efficient search of the database as our experimental results indicate.
282 citations
TL;DR: An image database in which images are indexed and retrieved based on color histograms, and a powerful indexing scheme where each histogram of an image is encoded into a numerical key, and stored in a two-layered tree structure is introduced.
Abstract: While general object recognition is difficult, it is relatively easy to capture various primitive properties such as color distributions, prominent regions and their topological features from an image and use them to narrow down the search space when attempts to retrieving images by contents from an image database are made. In this paper, we present an image database in which images are indexed and retrieved based on color histograms. We first address the problems inherent in color histograms created by the conventional method, and then propose a new method to create histograms which are compact in size and insensitive to minor illumination variations such as highlight, shape, and etc. A powerful indexing scheme where each histogram of an image is encoded into a numerical key, and stored in a two-layered tree structure is introduced. This approach turns the problem of histogram matching, which is computation intensive, into index key search, so as to realize quick data access in a large scale image database. Two types of user interfaces, Query by user provided sample images, and Query by combination of the system provided templates, are provided to meet various user requests. Various experimental evaluations exhibit the effectiveness of the image database system.
115 citations
TL;DR: The architecture of I2Cnet, a network of servers which provide content-based query services through a WWW browser, is discussed, with results showing the query response time of AttributeMatch, obtained with image classes of various sizes, and the degree of similarity of retrieved images to the query image under different similarity criteria.
78 citations
25 Aug 1996
TL;DR: This paper model the face detection problem using information theory, and formulate information based measures for detecting faces by maximizing the feature class separation, which is empirically compared using multiple test sets.
Abstract: Face detection in complex environments is an unsolved problem which has fundamental importance to face recognition, model based video coding, content based image retrieval, and human computer interaction. In this paper we model the face detection problem using information theory, and formulate information based measures for detecting faces by maximizing the feature class separation. The underlying principle is that search through an image can be viewed as a reduction of uncertainty in the classification of the image. The face detection algorithm is empirically compared using multiple test sets, which include four face databases from three universities.
57 citations
TL;DR: A novel image coding scheme is presented which captures some of this locally correlated color information and improves the selectivity of the retrieval mechanism -- an important issue for very large databases.
Abstract: Color histograms have been shown to be very effective in representing images for content- based retrieval systems They are compact, robust and amenable to computer analysis However, such histograms convey only global image properties and do not embody local color information which is so important when comparing and contrasting images We present a novel image coding scheme which captures some of this locally correlated color information and improves the selectivity of the retrieval mechanism -- an important issue for very large databases The technique uses a histogram of features which represent frequently occurring local combinations color tuples occurring throughout the image It outperforms straight color histogram matching We outline the thrust of our approach and discuss the factors affecting the efficacy of the retrieval mechanism using a database of color images
56 citations
17 Jun 1996
TL;DR: This work proposes an image database with a fully automated keyword extraction function that can extract two different conceptual-level keywords from images using an image recognition technique and the introduction of a "transition probability" to raise the retrieval accuracy.
Abstract: Proposes an image database with a fully automated keyword extraction function. Our approach can extract two different conceptual-level keywords from images. One is the "conceptual keyword", which is extracted by an image recognition technique using the "state transition model", which is a hierarchical model. The other keyword is the "scene description keyword", which is extracted by primitive parameters of color segments. We also propose the introduction of a "transition probability" to raise the retrieval accuracy (precision). Moreover, we evaluate the retrieval accuracy of this image database through a retrieval experiment using about 170 scenery images.
44 citations
25 Aug 1996
TL;DR: The limitations of boundary based shape features, and an alternative shape characterization technique based on orientation radiograms are discussed, and a working image retrieval system based on this approach is described.
Abstract: For content based image retrieval using shape descriptors, most approaches so far extract shape information from a segmentation of the image. Shape features derived based on a specific segmentation are not suitable for images containing complex structures. Further, static segmentation based approaches are useful only for a small set of queries. In this paper we discuss the limitations of such boundary based shape features, and propose an alternative shape characterization technique based on orientation radiograms. A working image retrieval system based on this approach is described and sample results are presented for a full-image query.
16 Sep 1996
TL;DR: A two-stage statistical approach supporting content-based search in image databases by performing correspondence analysis and ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database.
Abstract: This paper describes a two-stage statistical approach supporting content-based search in image databases. The first stage performs correspondence analysis, a factor analysis method transforming image attributes into a reduced-size, uncorrelated factor space. The second stage performs ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.
14 Aug 1996
TL;DR: This work introduces an effective block-oriented image decomposition structure which can be used to represent image content in image database systems and discusses the application of this image data model to content-based image retrieval.
Abstract: We investigate approaches to support effective and efficient retrieval of image data based on content. We first introduce an effective block-oriented image decomposition structure which can be used to represent image content in image database systems. We then discuss the application of this image data model to content-based image retrieval. Using wavelet transforms to extract image features, significant content features can be extracted from image data through decorrelating the data in their pixel format into the frequency domain. Feature vectors of images can then be constructed. Content-based image retrieval is performed by comparing the feature vectors of the query image and the decomposed segments in database images. Our experimental analysis illustrates that the proposed block-oriented image representation offers a novel decomposition structure to be used to facilitate effective and efficient image retrieval.
08 Mar 1996
TL;DR: This work introduces a joint fractal coding technique, applicable to pairs of images, which can be used to determine the degree of their similarity, and observes that wavelets transform approach performs more effective on content- based similarity comparison on those images which contain strong texture features.
Abstract: Image compression techniques based on wavelet and fractal coding have been recognized significantly useful in image texture classification and discrimination. In fractal coding approach, each image is represented by a set of self-transformations through which an approximation of the original image can be reconstructed. These transformations of images can be utilized to distinguish images. The fractal coding technique can be extended to effectively determine the similarity between images. We introduce a joint fractal coding technique, applicable to pairs of images, which can be used to determine the degree of their similarity. Our experimental results demonstrate that fractal code approach is effective for content-based image retrieval. In wavelet transform approach, the wavelet transform decorrelates the image data into frequency domain. Feature vectors of images can be constructed from wavelet transformations, which can also be utilized to distinguish images through measuring distances between feature vectors. Our experiments indicate that this approach is also effective on content-based similarity comparison between images. More specifically, we observe that wavelets transform approach performs more effective on content- based similarity comparison on those images which contain strong texture features, where fractal coding approach performs relatively more uniformly well for various type of images.
TL;DR: A two-stage statistical approach for “exploring and explaining” a pictorial database, for content-based image retrieval systems and how correspondence analysis provides image classes and facilitates the understanding of the role of image primitives and attributes used to index pictures is presented.
14 Oct 1996
TL;DR: An automatic human face detection system where several methods are tested and compared to compare subimages of the image pyramid, spanned by the input image, with a set of 'nose-eye' templates.
Abstract: Automatic human face detection in digital images with a complex environment is still an unsolved problem in computer vision and pattern recognition. It has several uses, such as human face recognition, content based image retrieval and model based video coding. We present an automatic human face detection system where several methods are tested and compared. The underlying principle of the system is to compare subimages of the image pyramid, spanned by the input image, with a set of 'nose-eye' templates. However this comparison is not done on the entire set of subimages of the image pyramid, but on a small subset, which is defined by the 'local maxima method'. False positives are found by using a set of non-face templates. The system is tested on two databases, each include over 1000 images.
IBM1
TL;DR: This paper demonstrates a prototype application of content-based database searching, specifically customised for designers in the textile industry, that takes account of their particular needs, enabling them to operate in a visually stimulating manner that does not inhibit their creativity.
Abstract: This paper demonstrates a prototype application of content-based database searching, specifically customised for designers in the textile industry. It takes account of their particular needs, enabling them to operate in a visually stimulating manner that does not inhibit their creativity.
25 Aug 1996
TL;DR: The objective of the queries in this research was the 19th-century mass-produced studio portrait or carte-de-visite, whose front and back sides provide a testbed for gray level and binary images classes.
Abstract: Digital storage of large photo collections opens the way to computer-aided queries based on visual rather than thematic search patterns. The objective of our queries in this research was the 19th-century mass-produced studio portrait or carte-de-visite, whose front and back sides provide a testbed for gray level and binary images classes. We established a ground truth for detecting highly similar images (former copies) in different classes of B/W images. The results will serve as a reference benchmark for yet to be developed visual search methods. The similarity measure used for locating near-copies was the average distance in pixel intensity for shifted image pairs with normalized position, orientation, resolution and lighting. To measure the performance of possible hierarchical comparison and ranking protocols, we scanned in test sets of known copies and near-copies together with over a thousand similar format pictures. The results show that projections are highly effective and efficient in indexing raw image data: reduced dimensionality, low noise, conservation of pattern characteristics, separable x- and y-translation (best shift) and suitable for hierarchical indexing. A query by example WWW demo program with precalculated ranking result files was developed for visual inspection and evaluation of similar image location methods.
TL;DR: The notion of logical features and various features to enable content-based query processing in a distributed environment are described and a tool named SemCap is described for extracting the logical features semi-automatically from images stored in low-level formats.
Abstract: Images are being generated at an ever increasing rate by diverse military and civilian sources. A content-based image retrieval system is required to utilize information from the image repositories effectively. Content-based retrieval is characterized by several generic query classes. With the existence of the information superhighway, image repositories are evolving in a decentralized fashion on the Internet. This necessitates network transparent distributed access in addition to the content-based retrieval capability. Images stored in low-level formats such as vector and raster are referred to as physical images. Constructing interactive responses to user queries using physical images is not practical and robust. To overcome this problem, we introduce the notion of logical features and describe various features to enable content-based query processing in a distributed environment. We describe a tool named SemCap for extracting the logical features semi-automatically. We also propose an architecture and an application level communication protocol for distributed content-based retrieval. We describe the prototype implementation of the architecture and demonstrate its versatility on two distributed image collections.
TL;DR: This paper investigates an effective approach to the clustering of image data based on the technique of fractal image coding, a method first introduced in conjunction with Fractal image compression technique.
Abstract: Large visual database systems require effective and efficient ways of indexing and accessing visual data on the basis of content. In this process, significant features must first be extracted from image data in their pixel format. These features must then be classified and indexed to assist efficient access to image content. With the large volume of visual data stored in a visual database, image classification is a critical step to achieve efficient indexing and retrieval. In this paper, we investigate an effective approach to the clustering of image data based on the technique of fractal image coding, a method first introduced in conjunction with fractal image compression technique. A joint fractal coding technique, applicable to pairs of images, is used to determine the degree of their similarity. Images in a visual database can be categorized in clusters on the basis of their similarity to a set of iconic images. Classification metrics are proposed for the measurement of the extent of similarity among images. By experimenting on a large set of texture and natural images, we demonstrate the applicability of these metrics and the proposed clustering technique to various visual database applications.
TL;DR: It is shown that different image representations have serious effects on image retrieval performance, and a need of a common color image interchange format is indicated.
Abstract: Content based image retrieval techniques are being developed for automatic indexing and retrieval of images in many applications. One of the main features of an image is its dominant colors, hence the development of color based image retrieval techniques. In these techniques, images are indexed using their dominant colors and images with perceptually similar dominant colors to the query are retrieved. In a large image database, images may come from many different sources, may be captured using different devices and represented using different color spaces. These differences may be subtle, but result in different meanings of image data. If these differences are not accounted for, image retrieval performance may suffer. In existing systems, this factor is normally not considered. In this paper, we present various different image representations. We then discuss effects on retrieval performance using color histograms when images in a database are represented differently. It is shown that different image representations have serious effects on image retrieval performance. We discuss the conversion between different image representations, and information required to carry out these conversions. This information is normally not available in most current image formats, which indicates a need of a common color image interchange format.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
22 May 1996
TL;DR: A novel image coding scheme is presented which captures some of this locally correlated colour information and improves the selectivity of the retrieval mechanism-an important issue for very large databases.
Abstract: Colour histograms have been shown to be very effective in representing images for content-based retrieval systems. They are compact, robust and amenable to computer analysis. However, such histograms convey only global image properties and do not embody local colour information which is so important when comparing and contrasting images. We present a novel image coding scheme which captures some of this locally correlated colour information and improves the selectivity of the retrieval mechanism-an important issue for very large databases. The technique uses a histogram of features which represent local combinations of colour artifacts within an image, called colour n-grams. We outline the thrust of our approach and discuss the factors affecting the efficacy of the retrieval mechanism using a database of colour images. (6 pages)
TL;DR: The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query.
Abstract: A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Proceedings Article•
01 Jan 1996
TL;DR: A new distance between shapes represented by TA, namely the Median distance, is presented, specially devised to minimize the impact of small variations in the shapes.
Abstract: Turning Angles Representation (TA) is considered one of the most interestingmethods for representing object shapes in content-based image retrieval systems. Nevertheless, the distance commonly used to measure the similarity between shapes represented by TAs, the Euclidean one, is generally too sensitive to small variations in the shapes. In this paper we present a new distance between shapes represented by TA, namely the Median distance, specially devised to minimize the e ects of small variations in the shapes. Its analytical properties are discussed and experimental results are provided by comparison against those obtained by applying traditional techniques based on Euclidean distance. The Median distance has been implemented in the Automatic Image Storage and Retrieval (AISR) system, which allows storage and content-based retrieval of 2-D images.
TL;DR: State-of-the-art techniques for online information retrieval over the information superhighway are presented and the application of Artificial intelligence (Al) search techniques to locate and disseminate information in an effective manner is advocated.
Abstract: Efficient retrieval of information from the vast sea of distributed databases over the internet has assumed great importance with the convergence of computer and communication technologies This paper presents some state-of-the-art techniques for online information retrieval over the information superhighway It further advocates the application of Artificial intelligence (Al) search techniques to locate and disseminate information in an effective manner Content based image retrieval can also benefit from Al techniques by utilising evolutionary programming for feature extraction and object recognition http://dxdoiorg/1014429/dbit1643273
24 Jan 1996
TL;DR: The design to convert the agent model to C++CL platform, an object oriented framework layered on top of PVM which allows planned and unplanned reconfiguration of distributed systems, is described.
Abstract: We describe a hybrid system for dealing with content-based retrieval from a library of images. An agent architecture is used in conjunction with a neural network to match and retrieve images. We explain how we define and use our image indices which allows us to treat the problem in a distributed fashion. We combine the indices with a multiple agent approach to help reduce our search space and to retrieve an image as a parallel action. The resulting system is one which is flexible but controlled. A neural network is used to generate a feature vector from the images which is used by the agents. The agents are based on the theories behind Lilies. Lilies (Localisation and InterLeaving strategies) was developed to deal with multiple agents for a planning environment. Lilies was used to prototype the initial representation of the agents used for searching the database. Although an implementation of the search-agents has been developed in Lilies, we also describe in this paper the design to convert the agent model to C++CL platform. C++CL is an object oriented framework layered on top of PVM which allows planned and unplanned reconfiguration of distributed systems. We introduce two issues of distributed systems: a neural net, for image compression; and the design of an agent searching architecture using C++CL.
TL;DR: The designed framework permits the development of new CBIR systems by utilizing existing components and by building new standardized components, utilizing atomic, re-usable software components.
Abstract: It is very hard to realize a general purpose, domain independent content-based image retrieval system. To implement a CBIR system rapidly for some specific purpose, tools that support application development are needed. The objective of this study has been to design a framework for content-based image retrieval application development. With this environment it is fast and easy to implement prototype systems and test their performance and usability. In order to speed up experiments, a compete computational chain has been implemented in such a way that rapid changes are possible. This has been realized utilizing atomic, re-usable software components. The designed framework permits the development of new CBIR systems by utilizing existing components and by building new standardized components.
11 Sep 1996
TL;DR: This work presents an integrated representation for color find local orientation, achieving robustness to several illumination conditions for free, and involves steerable filter techniques and Lab-color space conversion.
Abstract: Observing the development of content based image retrieval systems hindered by the lack of efficient image representations, color histogram based indexing techniques have been used quite successfully. Though their performance strongly depends on illumination conditions being controlled, there has been only small effort to make them invariant to illumination. By introducing color-orientation histograms we present an integrated representation for color find local orientation, achieving robustness to several illumination conditions for free. Our method involves steerable filter techniques and Lab-color space conversion.
TL;DR: This paper combines GEP-2D strings and usual 2D strings into generalized combined 2Dstrings to represent 2D images in order to capture both the absolute and relative spatial relationships in the image.
Abstract: In this paper we model each 2D image as a generalized extended pseudo-symbolic picture based on the absolute spatial relationships in the image. Each generalized extended pseudo-symbolic picture can then be represented by a GEP-2D string. We combine GEP-2D strings and usual 2D strings into generalized combined 2D strings to represent 2D images in order to capture both the absolute and relative spatial relationships in the image. Then we address how to maintain the complete information about the absolute spatial relationships in the image. Picture retrieval by generalized combined 2D strings is discussed.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.