  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.
