Single color extraction and image query
23 Oct 1995-Vol. 3, pp 3528
TL;DR: This approach identifies the regions within images that contain colors from predetermined color sets by searching over a large number of color sets, which allows very fast indexing of the image collection by the color contents of the images.
Abstract: We propose a method for automatic color extraction and indexing to support color queries of image and video databases. This approach identifies the regions within images that contain colors from predetermined color sets. By searching over a large number of color sets, a color index for the database is created in a fashion similar to that for file inversion. This allows very fast indexing of the image collection by the color contents of the images. Furthermore, information about the identified regions, such as the color set, size, and location, enables a rich variety of queries that specify both color content and spatial relationships of regions. We present the single color extraction and indexing method and contrast it to other color approaches. We examine single and multiple color extraction and image query on a database of 3000 color images.
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TL;DR: The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval.
Abstract: This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested.
2,197 citations
Cites background or methods from "Single color extraction and image q..."
...The relationship between the proposed color sets and the conventional color histogram was further discussed [139, 140]....
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...A more sophisticated approach is to segment the image into regions with salient color features by color set back-projection and then to store the position and color set feature of each region to support later queries [139]....
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...To facilitate fast search over large-scale image collections, Smith and Chang proposed color sets as an approximation to the color histogram [139, 140]....
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TL;DR: Results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects are presented.
Abstract: Retrieving images from large and varied collections using image content as a key is a challenging and important problem We present a new image representation that provides a transformation from the raw pixel data to a small set of image regions that are coherent in color and texture This "Blobworld" representation is created by clustering pixels in a joint color-texture-position feature space The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images We describe a system that uses the Blobworld representation to retrieve images from this collection An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics By finding image regions that roughly correspond to objects, we allow querying at the level of objects rather than global image properties We present results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects
1,574 citations
Cites background from "Single color extraction and image q..."
...Other examples of systems that identify materials using low-level image properties include Virage [17], VisualSEEk [39], Candid [24], and Chabot [30]....
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TL;DR: This work indexes the blob descriptions using a lower-rank approximation to the high-dimensional distance to make large-scale retrieval feasible, and shows encouraging results for both querying and indexing.
Abstract: Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Queryingi s based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions usinga tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both queryinga nd indexing.
896 citations
Cites methods from "Single color extraction and image q..."
...Many current image retrieval systems perform retrieval based primarily on lowlevel image features, including IBM's Query by Image Content (QBIC) [6], Photobook [19], Virage [9], VisualSEEk [23], Candid [15], and Chabot [18]....
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TL;DR: This work proposes a technique by which the color content of images and videos is automatically extracted to form a class of meta-data that is easily indexed and evaluates the retrieval effectiveness of the color set back-projection method and compares its performance to other color image retrieval methods.
Abstract: The growth of digital image and video archives is increasing the need for tools that effectively filter and efficiently search through large amounts of visual data. Towards this goal we propose a technique by which the color content of images and videos is automatically extracted to form a class of meta-data that is easily indexed. The color indexing algorithm uses the back- projection of binary color sets to extract color regions from images. This technique provides for both the automated extraction of regions and representation of their color content. It overcomes some of the problems with color histogram techniques such as high-dimensional feature vectors, spatial localization, indexing and distance computation. We present the binary color set back-projection technique and discuss its implementation in the VisualSEEk content- based image/video retrieval system for the World Wide Web. We also evaluate the retrieval effectiveness of the color set back-projection method and compare its performance to other color image retrieval methods.
588 citations
TL;DR: A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function.
Abstract: A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases.
320 citations
Cites methods from "Single color extraction and image q..."
...Although their method is robust to object occlusion and image resolution, but it is still sensitive to illumination changes [4]....
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References
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01 Jun 1993
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
15,645 citations
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TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.
Abstract: Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks.
This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.
5,672 citations
Book•
01 Jan 2011
TL;DR: In this paper, the acquisition and use of digital images in a wide variety of scientific fields is discussed. But the focus is on high dynamic range imaging in more than two dimensions.
Abstract: "This guide clearly explains the acquisition and use of digital images in a wide variety of scientific fields. This sixth edition features new sections on selecting a camera with resolution appropriate for use on light microscopes, on the ability of current cameras to capture raw images with high dynamic range, and on imaging in more than two dimensions. It discusses Dmax for X-ray images and combining images with different exposure settings to further extend the dynamic range. This edition also includes a new chapter on shape measurements, a review of new developments in image file searching, and a wide range of new examples and diagrams"
3,017 citations
Book•
01 Jan 1992TL;DR: The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.
Abstract: Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more. Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition: Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.
1,858 citations
01 Jul 1994
TL;DR: A set of novel features and similarity measures allowing query by image content, together with the QBIC system, and a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance.
Abstract: In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, shape, position, and dominant edges of image objects and regions. Potential applications include medical (“Give me other images that contain a tumor with a texture like this one”), photo-journalism (“Give me images that have blue at the top and red at the bottom”), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by image content, together with the QBIC system we implemented. We demonstrate the effectiveness of our system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also present new methods for efficient processing of QBIC queries that consist of filtering and indexing steps. We specifically address two problems: (a) non Euclidean distance measures; and (b) the high dimensionality of feature vectors. For the first problem, we introduce a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance. For the second, we illustrate how orthogonal transforms, such as Karhunen Loeve, can help reduce the dimensionality of the search space. Our methods are general and allow some “false hits” but no false dismissals. The resulting QBIC system offers effective retrieval using image content, and for large image databases significant speedup over straightforward indexing alternatives. The system is implemented in X/Motif and C running on an RS/6000.
1,285 citations