Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance
AbstractA 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 from 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 each image of the database. 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 its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Pham, M.-T.; Mercier, G.; Bombrun, L. Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance. J. Imaging 2017, 3, 43.
Pham M-T, Mercier G, Bombrun L. Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance. Journal of Imaging. 2017; 3(4):43.Chicago/Turabian Style
Pham, Minh-Tan; Mercier, Grégoire; Bombrun, Lionel. 2017. "Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance." J. Imaging 3, no. 4: 43.