Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Minh-Tan Pham 1 Grégoire Mercier 2 Lionel Bombrun 3
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 Lab-STICC_TB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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 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.
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Submitted on : Monday, November 6, 2017 - 10:48:15 AM
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Minh-Tan Pham, Grégoire Mercier, Lionel Bombrun. Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance . Journal of Imaging, MDPI, 2017, 3 (4), pp.43. ⟨10.3390/jimaging3040043⟩. ⟨hal-01629218⟩

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