Abstract not found

vision algorithms. It usually is a process that starts at a pixel-level, and that involves a pre-process of colorspace transformation followed by a classification process. A colorspace transformation is assumed to increase separability between skin and non-skin classes, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the colorspace transformation does bring those benefits by measuring four separability measurements on a large dataset of 805 images with different skin tones and illumination. Surprising results indicate that most of the colorspace transformations do not bring the benefits which have been assumed. 1 1.

Color is a feature of the great majority of content-based image retrieval systems. However the robustness, effectiveness, and efficiency of its use in image indexing are still open issues. This paper provides a comprehensive survey of the methods for color image indexing and retrieval described in the literature. In particular, image preprocessing, the features used to represent color information, and the measures adopted to compute the similarity between the features of two images are critically analyzed.

Computers should be able to detect and track the articulated 3-D pose of a human being moving through a video sequence. Incremental tracking methods often prove slow and unreliable, and many must be initialized by a human operator before they can track a sequence. This paper describes a simple yet effective algorithm for tracking articulated pose, based upon looking up observations (such as body silhouettes) within a collection of known poses. The new algorithm runs quickly, can initialize itself without human intervention, and can automatically recover from critical tracking errors made while tracking previous frames in a video sequence. Key words: monocular tracking, articulated tracking, pose tracking, silhouette lookup, failure recovery 1

this paper, we briefly summarize those aspects and present a set of experiments that thoroughly evaluates the FIDS techniques and system.

Abstract—We define similar video content as video sequences with almost identical content but possibly compressed at different qualities, reformatted to different sizes and frame-rates, undergone minor editing in either spatial or temporal domain, or summarized into keyframe sequences. Building a search engine to identify such similar content in the World-Wide Web requires: 1) robust video similarity measurements; 2) fast similarity search techniques on large databases; and 3) intuitive organization of search results. In a previous paper, we proposed a randomized technique called the video signature (ViSig) method for video similarity measurement. In this paper, we focus on the remaining two issues by proposing a feature extraction scheme for fast similarity search, and a clustering algorithm for identification of similar clusters. Similar to many other content-based methods, the ViSig method uses high-dimensional feature vectors to represent video. To warrant a fast response time for similarity searches on high dimensional vectors, we propose a novel nonlinear feature extraction scheme on arbitrary metric spaces that combines the triangle inequality with the classical Principal Component Analysis (PCA). We show experimentally that the proposed technique outperforms PCA, Fastmap, Triangle-Inequality Pruning, and Haar wavelet on signature data. To further improve retrieval performance, and provide better organization of similarity search results, we introduce a new graph-theoretical clustering algorithm on large databases of signatures. This algorithm treats all signatures as an abstract threshold graph, where the distance threshold is determined based on local data statistics. Similar clusters are then identified as highly connected regions in the graph. By measuring the retrieval performance against a ground-truth set, we show that our proposed algorithm outperforms simple thresholding, single-link and complete-link hierarchical clustering techniques. Index Terms—Clustering, dimension reduction, similarity search, video signature, web search. I.

Video signatures are compact representations of video sequences designed for efficient similarity measurement. In this paper, we propose a feature extraction technique to support fast similarity search on large databases of video signatures. Our proposed technique transforms the high dimensional video signatures into low dimensional vectors where similarity search can be efficiently performed. We exploit both the upper and lower bounds of the triangle inequalities in approximating the high-dimensional metric, and combine this approximation with the classical PCA to achieve the target dimension. Experimental results on a large set of web video sequences show that our technique outperforms Fastmap, Haar wavelet, PCA, and Triangle-Inequality Pruning. 1.

Considering the fact that majority of images stored in computers , such as those from the Internet, are in JPEG compressed format, it would be highly desirable to have the content-based image indexing and retrieval conducted directly in compressed domain. In this paper, we present a partial decoding algorithm to index images directly in compressed domain for all JPEG compressed images. Following that, an empirical study is also carried out to compare the performance of such approaches in DCT domain and that of original images in pixel domain. This technology will prove invaluable in applications where fast image key generation is required such as on the Internet or embedded in future imaging equipment. Necessary as not only will the amount of media available on the Internet continue to grow but also the amounts stored in homes - on digital televisions, computers, cameras and media.

motion analysis. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence of the absence of the illuminance component, and the two color modeling approaches are compared. The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 805 images with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained by transforming the pixel color to the SCT or HSI colorspaces, keeping the illuminance component, and modeling the color with the histogram approach.

A content-based image retrieval(CBIR) system has been constructed to integrate relevance feedback with triangle-inequality based algorithms. The system offers typically 20 to 30 times faster retrieving speed with minimum sacrifice of retrieval performance on Corel database consisting of more than 17,000 images. The theoretic framework is built by using triangleinequality based algorithms at sub-feature level and using relevance feedback techniques at feature level. Results show retrieval performance is clearly improved over the approach with only triangle-inequality based algorithms. A new high level weight updating method for the hierarchical distance model for relevance feedback is proposed.