Local features are successfully used in 3D shape retrieval by encoding features descriptors into global shape signatures. Previous 3D retrieval systems use different encoding methods, such as histogram encoding and Fisher encodings, making it difficult to evaluate one encoding technique against another. We perform a comparative analysis of four recent encoding methods when used in shape retriev...

This paper presents a strategy for shape-based image retrieval in which moment invariants form a feature vector to describe the shape of an object. Fuzzy k-means clustering is used to group similar images in an image collection into k-clusters whereas neural network is used to facilitate efficient retrieval of similar images against a given user-provided query image. Retrieval results and perfo...

This paper presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Since shape is one of the most widely used image feature exploited in content-based image retrieval systems, we studied for each descriptor, the number of coefficients needed for indexing and their retrieval performance. Specifically, we studied Fourier, ...

This paper presents a 3D shape retrieval algorithm based on the Bag of Words (BoW) paradigm. For a given 3D shape, the proposed approach considers a set of feature points uniformly sampled on the surface and associated with local Fourier descriptors; this descriptor is computed in the neighborhood of each feature point by projecting the geometry onto the eigenvectors of the Laplace-Beltrami ope...

While supervised learning approaches for 3D shape retrieval have been successfully used to incorporate human knowledge about object classes based on global shape features, the incorporation of local features still remains a difficult task. First, it is not obvious how to measure the similarity between two objects each represented by a set of local features, and second, it is not clear how to ch...

We consider the problem of shape correspondence and retrieval. Although our focus is on articulated shapes, the methods developed are applicable to any shape specified as a contour, in the 2D case, or a surface mesh, in 3D. We propose separate methods for 2D and 3D shape correspondence and retrieval, but the basic idea for both is to characterize shapes using intrinsic measures, defined by geod...

Content Based Image Retrieval (CBIR) system using Region based shape descriptors is proposed in my work. Further, the image classification efficiency is improved by employing Support Vector Machine (SVM) classifier. In this paper we concentrate on region based shape descriptors. In Region based shape descriptors include Hu moments, Zernike Moments, and exact Legendre Moment. In CBIR system the ...

In this paper, we describe an incipient method for image retrieval predicated on the local invariant shape feature, designated scalable shape context. The feature utilizes the Harris-Laplace corner to locat the fix points and coinside scale in the animal and flower image. Then, we utilize shape context to explain the local shape. Correspondence of feature points is achieved by a weighted bipart...

For content based image retrieval using shape descriptors, most approaches so far extract shape information from a segmentation of the image. Shape features derived based on a specific segmentation are not suitable for images containing complex structures. Further, static segmentation based approaches are useful only for a small set of queries. In this paper we discuss the limitations of such b...

Content-Based Image Retrieval (CBIR) uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. Active research in CBIR is geared towards the development of methodologies for analyzing, interpreting cataloging and indexing image databases. In addition to their development, efforts are also being made to evaluate the performance of im...