Table of Contents
J. Imaging, Volume 3, Issue 4 (December 2017)
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Cover Story (view full-size image) This paper proposes a novel method to tackle content-based image retrieval (CBIR) task using both [...] Read more. This paper proposes a novel method to tackle content-based image retrieval (CBIR) task using both texture and color features. The main motivation is to represent and characterize an image by a set of local descriptors extracted from characteristic points (i.e., keypoints) within the image. Then, the dissimilarity measure is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds) formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and exploit the local extrema pixels (i.e., local maximum and local minimum pixels in terms of intensity) as our feature points. We then construct the local extrema-based descriptor (LED) for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. As a result, each image is encoded by an LED feature point cloud and the Riemannian distances between these point clouds enable us to tackle CBI