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Book ChapterDOI

Novel Feature Extraction Strategies Supporting 2D Shape Description and Retrieval

TL;DR: This chapter contributes and relatively compares three shape descriptors that are further tested for shape classification by employing a supervised machine learning mechanism, thereby signifying the robustness of these descriptors towards diverse affine transformations thereby, making it suitable for dynamic CBIR applications.
Abstract: Acute shape characterization in image retrieval tasks remain a persisting issue in computer vision determining their retrieval performance. This chapter contributes and relatively compares three such descriptors that are further tested for shape classification by employing a supervised machine learning mechanism. The core objective of this chapter is the effective exploitation of simple computing concepts for realizing shape descriptors aiding retrieval. Accordingly, simple and novel shape descriptors with its performance analysis are presented in this chapter. The potency of these methods is investigated using the Bull’s Eye Retrieval (BER) rate on benchmarked datasets such as the Kimia, MPEG-7 CE Shape-1 part B and Tari-1000. Consistent BER greater than 90% attained across the diverse datasets affirms the descriptors efficacy, consequently signifying the robustness of these descriptors towards diverse affine transformations thereby, making it suitable for dynamic CBIR applications.
References
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Journal ArticleDOI
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.

6,693 citations

Journal ArticleDOI
TL;DR: This paper identifies some promising techniques for image retrieval according to standard principles and examines implementation procedures for each technique and discusses its advantages and disadvantages.

1,910 citations

Journal ArticleDOI
TL;DR: It is suggested that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts.
Abstract: Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms

1,123 citations

Journal ArticleDOI
TL;DR: A shape retrieval method using triangle-area representation for nonrigid shapes with closed contours that is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, and scaling, and robust against noise and moderate amounts of occlusion.

278 citations

Book ChapterDOI
21 Jul 2004
TL;DR: A detailed evaluation of the use of texture features in a query-by-example approach to image retrieval using 3 radically different texture feature types motivated by statistical, psychological and signal processing points of view is carried out.
Abstract: We have carried out a detailed evaluation of the use of texture features in a query-by-example approach to image retrieval. We used 3 radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view. The features were evaluated and tested on retrieval tasks from the Corel and TRECVID2003 image collections. For the latter we also looked at the effects of combining texture features with a colour feature.

251 citations