Showing papers on "Content-based image retrieval published in 2010"

New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative

Abstract: The MIR Flickr collection consists of 25000 high-quality photographic images of thousands of Flickr users, made available under the Creative Commons license. The database includes all the original user tags and EXIF metadata. Additionally, detailed and accurate annotations are provided for topics corresponding to the most prominent visual concepts in the user tag data. The rich metadata allow for a wide variety of image retrieval benchmarking scenarios.In this paper, we provide an overview of the various strategies that were devised for automatic visual concept detection using the MIR Flickr collection. In particular we discuss results from various experiments in combining social data and low-level content-based descriptors to improve the accuracy of visual concept classifiers. Additionally, we present retrieval results obtained by relevance feedback methods, demonstrating (i) how their performance can be enhanced using features based on visual concept classifiers, and (ii) how their performance, based on small samples, can be measured relative to their large sample classifier counterparts.Additionally, we identify a number of promising trends and ideas in visual concept detection. To keep the MIR Flickr collection up-to-date on these developments, we have formulated two new initiatives to extend the original image collection. First, the collection will be extended to one million Creative Commons Flickr images. Second, a number of state-of-the-art content-based descriptors will be made available for the entire collection.

Content-based image retrieval using color moment and Gabor texture feature

Abstract: Aim to currently content-based image retrieval method having high computational complexity and low retrieval accuracy problem, this paper proposes a content-based image retrieval method based on color and texture features. As its color features, color moments of the Hue, Saturation and Value (HSV) component images in HSV color space are used. As its texture features, Gabor texture descriptors are adopted. Users assign the weights to each feature respectively and calculate the similarity with combined features of color and texture according to normalized Euclidean distance. Experiment results show that the proposed method has higher retrieval accuracy than conventional methods using color and texture features even though its feature vector dimension results in a lower rate than the conventional method.

Semi-supervised distance metric learning for collaborative image retrieval and clustering

Abstract: Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are limited and noisy. We formally formulate the learning problem into a convex optimization task and then present a new technique, named as “Laplacian Regularized Metric Learning” (LRML). Two efficient algorithms are then proposed to solve the LRML task. Further, we apply the proposed technique to two applications. One direct application is for Collaborative Image Retrieval (CIR), which aims to explore the CBIR log data for improving the retrieval performance of CBIR systems. The other application is for Collaborative Image Clustering (CIC), which aims to explore the CBIR log data for enhancing the clustering performance of image pattern clustering tasks. We conduct extensive evaluation to compare the proposed LRML method with a number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information. Encouraging results validate the effectiveness of the proposed technique.

Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval

Abstract: With many potential multimedia applications, content-based image retrieval (CBIR) has recently gained more attention for image management and Web search. A wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user's preferences and bridge the semantic gap. However, there is still a big room to further the RF performance, because the popular RF algorithms ignore the manifold structure of image low-level visual features. In this paper, we propose the biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features. BDEE precisely models both the intraclass geometry and interclass discrimination and never meets the undersampled problem. To consider unlabelled samples, a manifold regularization-based item is introduced and combined with BDEE to form the semi-supervised BDEE, or semi-BDEE for short. To justify the effectiveness of the proposed BDEE and semi-BDEE, we compare them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery.

Real-Time Visual Concept Classification

Abstract: As datasets grow increasingly large in content-based image and video retrieval, computational efficiency of concept classification is important. This paper reviews techniques to accelerate concept classification, where we show the trade-off between computational efficiency and accuracy. As a basis, we use the Bag-of-Words algorithm that in the 2008 benchmarks of TRECVID and PASCAL lead to the best performance scores. We divide the evaluation in three steps: 1) Descriptor Extraction, where we evaluate SIFT, SURF, DAISY, and Semantic Textons. 2) Visual Word Assignment, where we compare a k-means visual vocabulary with a Random Forest and evaluate subsampling, dimension reduction with PCA, and division strategies of the Spatial Pyramid. 3) Classification, where we evaluate the χ2, RBF, and Fast Histogram Intersection kernel for the SVM. Apart from the evaluation, we accelerate the calculation of densely sampled SIFT and SURF, accelerate nearest neighbor assignment, and improve accuracy of the Histogram Intersection kernel. We conclude by discussing whether further acceleration of the Bag-of-Words pipeline is possible. Our results lead to a 7-fold speed increase without accuracy loss, and a 70-fold speed increase with 3% accuracy loss. The latter system does classification in real-time, which opens up new applications for automatic concept classification. For example, this system permits five standard desktop PCs to automatically tag for 20 classes all images that are currently uploaded to Flickr.

Bridging the Semantic Gap Between Image Contents and Tags

Abstract: With the exponential growth of Web 2.0 applications, tags have been used extensively to describe the image contents on the Web. Due to the noisy and sparse nature in the human generated tags, how to understand and utilize these tags for image retrieval tasks has become an emerging research direction. As the low-level visual features can provide fruitful information, they are employed to improve the image retrieval results. However, it is challenging to bridge the semantic gap between image contents and tags. To attack this critical problem, we propose a unified framework in this paper which stems from a two-level data fusions between the image contents and tags: 1) A unified graph is built to fuse the visual feature-based image similarity graph with the image-tag bipartite graph; 2) A novel random walk model is then proposed, which utilizes a fusion parameter to balance the influences between the image contents and tags. Furthermore, the presented framework not only can naturally incorporate the pseudo relevance feedback process, but also it can be directly applied to applications such as content-based image retrieval, text-based image retrieval, and image annotation. Experimental analysis on a large Flickr dataset shows the effectiveness and efficiency of our proposed framework.

A Survey On: “Content Based Image Retrieval Systems”

Abstract: network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analysed and compared. It also introduced the feature like neuro fuzzy technique, color histogram, texture and edge density for accurate and effective Content Based Image Retrieval System.

Building a web-scale image similarity search system

Abstract: As the number of digital images is growing fast and Content-based Image Retrieval (CBIR) is gaining in popularity, CBIR systems should leap towards Web-scale datasets. In this paper, we report on our experience in building an experimental similarity search system on a test collection of more than 50 million images. The first big challenge we have been facing was obtaining a collection of images of this scale with the corresponding descriptive features. We have tackled the non-trivial process of image crawling and extraction of several MPEG-7 descriptors. The result of this effort is a test collection, the first of such scale, opened to the research community for experiments and comparisons. The second challenge was to develop indexing and searching mechanisms able to scale to the target size and to answer similarity queries in real-time. We have achieved this goal by creating sophisticated centralized and distributed structures based purely on the metric space model of data. We have joined them together which has resulted in an extremely flexible and scalable solution. In this paper, we study in detail the performance of this technology and its evolvement as the data volume grows by three orders of magnitude. The results of the experiments are very encouraging and promising for future applications.

Mobile product recognition

Abstract: We present a mobile product recognition system for the camera-phone. By snapping a picture of a product with a camera-phone, the user can retrieve online information of the product. The product is recognized by an image-based retrieval system located on a remote server. Our database currently comprises more than one million entries, primarily products packaged in rigid boxes with printed labels, such as CDs, DVDs, and books. We extract low bit-rate descriptors from the query image and compress the location of the descriptors using location histogram coding on the camera-phone. We transmit the compressed query features, instead of a query image, to reduce the transmission delay. We use inverted index compression and fast geometric re-ranking on our database to provide a low delay image recognition response for large scale databases. Experimental timing results on different parts of the mobile product recognition system is reported in this work.

User intention modeling for interactive image retrieval

Abstract: We propose three innovative interactive methods to let computer better understand user intention in content-based image retrieval: 1. Smart intention list induces user intention, thereby improves search results by intention-specific search schema; 2. Reference strokes interaction allows user to specify in detail about the intention by pointing out interested regions; 3. Natural user feedback easily collects data of user relevance feedbacks to boost the performance of the system. Systematic user study shows that the proposed interactive mechanism improves search efficiency, reduces user workload, and enhances user experience.

A next generation browsing environment for large image repositories

Abstract: Next generation environments will change the way people work and live as they will provide new advances in areas ranging from remote work and education, e-commerce, gaming to information-on-demand. In many of these applications intelligent interpretation of multimedia data such as image, video and audio resources is necessary. In this paper we present an effective approach to handling image repositories providing the user with an intuitive interface of visualising and browsing large collections of pictures. Based on the idea of similarity-based organisation of images where images that are visually similar are located close to each other in visualisation space, images are projected onto a sphere with which the user can interact. Rotating the sphere reveals images of different colours while tilting operations focus on brighter or darker images. Large image collections are handled through a hierarchical approach that brings up similar, previously hidden, images when zooming in on an area. Furthermore, the way images are organised can be interactively changed by the user. Our next generation browsing environment has been successfully tested on a large database of several thousand images.

A review of content-based image retrieval

Abstract: A comprehensive survey on patch recognition, which is a crucial part of content-based image retrieval (CBIR), is presented. CBIR can be viewed as a methodology in which three correlated modules including patch sampling, characterizing, and recognizing are employed. This paper aims to evaluate meaningful models for one of the most challenging problems in image understanding, specifically, for the effective and efficient mapping between image visual features and high-level semantic concepts. To achieve this, the latest classification, clustering, and interactive methods have been meticulously discussed. Finally, several recommendations for future research issues have been suggested based on the weaknesses of recent technologies.

A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization

Abstract: Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the ?semantic gap? problem. In this paper, an innovative approach is proposed that combines a relevance feedback (RF) approach with an evolutionary stochastic algorithm, called particle swarm optimizer (PSO), as a way to grasp user's semantics through optimized iterative learning. The retrieval uses human interaction to achieve a twofold goal: 1) to guide the swarm particles in the exploration of the solution space towards the cluster of relevant images; 2) to dynamically modify the feature space by appropriately weighting the descriptive features according to the users' perception of relevance. Extensive simulations showed that the proposed technique outperforms traditional deterministic RF approaches of the same class, thanks to its stochastic nature, which allows a better exploration of complex, nonlinear, and highly-dimensional solution spaces.

Plant species identification using leaf image retrieval

Abstract: In this paper, an effective shape-based leaf image retrieval system is presented. A new contour descriptor is defined which reduces the number of points for the shape representation considerably. This shape representation is based on the curvature of the leaf contour and it deals with the scale factor in a novel and compact way. A two-step algorithm for retrieval is used. In a first step, the database is reduced using some geometrical features. Then a similarity measure between the contour representations is used to rank conveniently leaf images on the database. We implemented a prototype system based on these features and performed several experiments to show its effectiveness for plant species identification.

Learning to rank for content-based image retrieval

Abstract: In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results The typical ranking strategy used by many CBIR systems is to employ image content descriptors, so that returned images that are most similar to the query image are placed higher in the rank While this strategy is well accepted and widely used, improved results may be obtained by combining multiple image descriptors In this paper we explore this idea, and introduce algorithms that learn to combine information coming from different descriptors The proposed learning to rank algorithms are based on three diverse learning techniques: Support Vector Machines (CBIR-SVM), Genetic Programming (CBIR-GP), and Association Rules (CBIR-AR) Eighteen image content descriptors(color, texture, and shape information) are used as input and provided as training to the learning algorithms We performed a systematic evaluation involving two complex and heterogeneous image databases (Corel e Caltech) and two evaluation measures (Precision and MAP) The empirical results show that all learning algorithms provide significant gains when compared to the typical ranking strategy in which descriptors are used in isolation We concluded that, in general, CBIR-AR and CBIR-GP outperforms CBIR-SVM A fine-grained analysis revealed the lack of correlation between the results provided by CBIR-AR and the results provided by the other two algorithms, which indicates the opportunity of an advantageous hybrid approach

Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

Abstract: Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).

Adaptive Color Feature Extraction Based on Image Color Distributions

Abstract: This paper proposes an adaptive color feature extraction scheme by considering the color distribution of an image. Based on the binary quaternion-moment-preserving (BQMP) thresholding technique, the proposed extraction methods, fixed cardinality (FC) and variable cardinality (VC), are able to extract color features by preserving the color distribution of an image up to the third moment and to substantially reduce the distortion incurred in the extraction process. In addition to utilizing the earth mover's distance (EMD) as the distance measure of our color features, we also devise an efficient and effective distance measure, comparing histograms by clustering (CHIC). Moreover, the efficient implementation of our extraction methods is explored. With slight modification of the BQMP algorithm, our extraction methods are equipped with the capability of exploiting the concurrent property of hardware implementation. The experimental results show that our hardware implementation can achieve approximately a second order of magnitude improvement over the software implementation. It is noted that minimizing the distortion incurred in the extraction process can enhance the accuracy of the subsequent various image applications, and we evaluate the meaningfulness of the new extraction methods by the application to content-based image retrieval (CBIR). Our experimental results show that the proposed extraction methods can enhance the average retrieval precision rate by a factor of 25% over that of a traditional color feature extraction method.

TOP-SURF: a visual words toolkit

Abstract: TOP-SURF is an image descriptor that combines interest points with visual words, resulting in a high performance yet compact descriptor that is designed with a wide range of content-based image retrieval applications in mind. TOP-SURF offers the flexibility to vary descriptor size and supports very fast image matching. In addition to the source code for the visual word extraction and comparisons, we also provide a high level API and very large pre-computed codebooks targeting web image content for both research and teaching purposes.

Automatic Color Based Reassembly of Fragmented Images and Paintings

Abstract: The problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology, the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is very difficult, as it requires great amount of time, skill and effort. Thus, the automation of such a work is very important and can lead to faster, more efficient, painting reassembly and to a significant reduction in the human effort involved. In this paper, an integrated method for automatic color based 2-D image fragment reassembly is presented. The proposed 2-D reassembly technique is divided into four steps. Initially, the image fragments which are probably spatially adjacent, are identified utilizing techniques employed in content based image retrieval systems. The second operation is to identify the matching contour segments for every retained couple of image fragments, via a dynamic programming technique. The next step is to identify the optimal transformation in order to align the matching contour segments. Many registration techniques have been evaluated to this end. Finally, the overall image is reassembled from its properly aligned fragments. This is achieved via a novel algorithm, which exploits the alignment angles found during the previous step. In each stage, the most robust algorithms having the best performance are investigated and their results are fed to the next step. We have experimented with the proposed method using digitally scanned images of actual torn pieces of paper image prints and we produced very satisfactory reassembly results.

Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

Abstract: Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).

Multi Feature Content Based Image Retrieval

Abstract: There are numbers of methods prevailing for Image Mining Techniques This Paper includes the features of four techniques I,e Color Histogram, Color moment, Texture, and Edge Histogram Descriptor The nature of the Image is basically based on the Human Perception of the Image The Machine interpretation of the Image is based on the Contours and surfaces of the Images The study of the Image Mining is a very challenging task because it involves the Pattern Recognition which is a very important tool for the Machine Vision system A combination of four feature extraction methods namely color Histogram, Color Moment, texture, and Edge Histogram Descriptor There is a provision to add new features in future for better retrieval efficiency In this paper the combination of the four techniques are used and the Euclidian distances are calculated of the every features are added and the averages are made The user interface is provided by the Mat lab The image properties analyzed in this work are by using computer vision and image processing algorithms For color the histogram of images are computed, for texture co occurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found For retrieval of images, the averages of the four techniques are made and the resultant Image is retrieved Keywords-component; Content Based Image Retrieval (CBIR), Edge Histogram Descriptor (EHD),Color moment ,textures, Color Histogram

An approach to content-based image retrieval based on the Lucene search engine library

Abstract: Content-based image retrieval is becoming a popular way for searching digital libraries as the amount of available multimedia data increases. However, the cost of developing from scratch a robust and reliable system with content-based image retrieval facilities for large databases is quite prohibitive. In this paper, we propose to exploit an approach to perform approximate similarity search in metric spaces developed by [3, 6]. The idea at the basis of these techniques is that when two objects are very close one to each other they 'see' the world around them in the same way. Accordingly, we can use a measure of dissimilarity between the views of the world at different objects, in place of the distance function of the underlying metric space. To employ this idea the low level image features (such as colors and textures) are converted into a textual form and are indexed into the inverted index by means of the Lucene search engine library. The conversion of the features in textual form allows us to employ the Lucene's off-the-shelf indexing and searching abilities with a little implementation effort. In this way, we are able to set up a robust information retrieval system that combines full-text search with content-based image retrieval capabilities.

Image Clustering and Retrieval using Image Mining Techniques

Abstract: Image retrieval is the basic requirement task in the present scenario. Content Based Image Retrieval is the popular image retrieval system by which the target image to be retrieved based on the useful features of the given image. In other end, image mining is the arising concept which can be used to extract potential information from the general collection of images. Target or close Images can be retrieved in a little fast if it is clustered in a right manner. In this paper, the concepts of CBIR and Image mining have been combined and a new clustering technique has been introduced in order to increase the speed of the image retrieval system.

Visual attention for implicit relevance feedback in a content based image retrieval

Abstract: In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users' eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and "pyramid" like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.