Texture and Color-based Image Retrieval Using the Local Extrema Features and Riemannian Distance

TL;DR

This paper introduces a novel content-based image retrieval method that uses local extrema features and Riemannian distances to effectively compare images based on texture and color information.

Contribution

It proposes a new local extrema-based descriptor and a geometric distance measure for improved image retrieval performance.

Findings

Achieved competitive results on texture databases

Demonstrated efficiency of local extrema features

Validated approach against state-of-the-art methods

Abstract

A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted at characteristic points (i.e. keypoints) within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e. manifolds) formed by the sets of local descriptors generated from these images. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED) is generated for each keypoint by integrating all color, spatial as well as gradient information captured by a set of its nearest local extrema. Hence, each image is encoded by a LED feature point cloud and riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on Vistex, Stex and colored Brodatz texture databases using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.