We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogramrobustly tolerates large changesin appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color, and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measures. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.

Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise. Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or “don’t care ” symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.

There is a growing need for new representations of video that allow not only compact storage of data but also content-based functionalities such as search and manipulation of objects. We present here a prototype system, called NeTra-V, that is currently being developed to address some of these content related issues. The system has a twostage video processing structure: a global feature extraction and clustering stage, and a local feature extraction and object-based representation stage. Key aspects of the system include a new spatio-temporal segmentation and objecttracking scheme, and a hierarchical object-based video representation model. The spatio-temporal segmentation scheme combines the color/texture image segmentation and affine motion estimation techniques. Experimental results show that the proposed approach can handle large motion. The output of the segmentation, the alpha plane as it is referred to in the MPEG-4 terminology, can be used to compute local image properties. Thi...

Color histogram matching has been shown to be a promising way of quickly indexing into a large image database. Yet, few experiments have been done to test the method on truly large databases, and even if they were performed, they would give little guidance to a user wondering if the technique would be useful with his or her database. In this paper we define and analyze measures relevant to extending color histogram indexing to large databases: capacity (how many distinguishable histograms can be stored) and sensitivity (how the average number of retrieved images depends on the retrieval threshold). The theoretical results lead to a practical test procedure which enables a user to determine the performance of color histogram indexing on a large database by looking at a small, randomly-chosen subset of the images. We suggest how our analysis can be extended to other feature-based indexing techniques. Capacity and Sensitivity of Color Histogram Indexing 1 1 Introduction As the cost of...

A technique to retrieve images by region matching using a combined feature index based on color, shape and location is presented within the framework of MPEG-7. Dominant regions within each image are indexed using integrated color, shape and location features. Various combinations of regions are also indexed. The resulting indices and related metadata are stored in a Hash structure, where similar images tend to form clusters. The retrieval process is non-cascading and images can be retrieved based on color, shape or location and also based on a combined colorshape -location index. Results obtained show that retrieval e#ectiveness increases in non-cascaded region-based querying by combined index.

This work addresses the issue of color feature selection for content-based retrieval from large, heterogeneous color image databases where no assumptions can be made about the images or the type of queries. The color features used to describe an image have been developed based on the need for speed in matching and ease of computation on complex images while maintaining invariance to differences in scale, orientation, and location of the queried object in the database images and also the presence of significant, interfering backgrounds. The colors present and their spatial relationships are used as features to describe a color image. These features are used in an efficient, multi-phase retrieval system to produce retrieval results fast enough for use with an online user. Test results with multi-colored query objects from manmade and natural domains highlight the capabilities of the system. This material is based on work supported in part by the National Science Foundation, Library of...

Image retrieval has found more and more applications. Due to the well recognized semantic gap problem, the accuracy and the recall of image similarity search are often still low. As an effective method to improve the quality of image retrieval, the relevance feedback approach actively applies users ’ feedback to refine the search. As searching a large image database is often costly, to improve the efficiency, high dimensional indexes may help. However, many existing database indexes are not adaptive to updates of distance measures caused by users ’ feedback. In this paper, we propose a demo to illustrate the relevance feedback based interactive images retrieval procedure, and examine the effectiveness and the efficiency of various indexes. Particularly, audience can interactively investigate the effect of updated distance measures on the data space where the images are supposed to be indexed, and on the distributions of the similar images in the indexes. We also introduce our new B +-tree-like index method based on cluster splitting and iDistance. 1. BACKGROUND Image retrieval is important in many applications. Typically, in a similarity search, a user wants to search for images that are similar to a given query image. However, due to the well recognized semantic gap problem [1], the accuracy and the recall of image similarity search are often still low. As an effective method to improve the quality of image retrieval, the relevance feedback approach [13] actively applies users ’ feedback to refine the search. In the first round, a

Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computational biology, this similarity hints at structure which has been conserved for some reason and may therefore be of interest. Since the formalism of time series motifs in 2002, dozens of researchers have used them for diverse applications in many different domains. Because the obvious algorithm for computing motifs is quadratic in the number of items, more than a dozen approximate algorithms to discover motifs have been proposed in the literature. In this work, for the first time, we show a tractable exact algorithm to find time series motifs. As we shall show through extensive experiments, our algorithm is up to three orders of magnitude faster than brute-force search in large datasets. We further show that our algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization, and we consider detailed case studies in domains as diverse as electroencephalograph interpretation and entomological telemetry data mining.