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Open AccessArticle

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

1
IRISA-Université Bretagne Sud, Campus de Tohannic, Rue Yves Mainguy, 56000 Vannes, France
2
TELECOM Bretagne, UMR CNRS 6285 Lab-STICC/CID, 29238 Brest CEDEX 3, France
3
CNRS-IMS Lab. (UMR 5218), University of Bordeaux, 33402 Talence CEDEX, France
*
Author to whom correspondence should be addressed.
J. Imaging 2017, 3(4), 43; https://doi.org/10.3390/jimaging3040043
Received: 28 August 2017 / Revised: 2 October 2017 / Accepted: 5 October 2017 / Published: 10 October 2017
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. View Full-Text
Keywords: content-based image retrieval (CBIR); pointwise approach; local extrema features; color textures; Riemannian distance (RD) content-based image retrieval (CBIR); pointwise approach; local extrema features; color textures; Riemannian distance (RD)
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MDPI and ACS Style

Pham, M.-T.; Mercier, G.; Bombrun, L. Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance. J. Imaging 2017, 3, 43. https://doi.org/10.3390/jimaging3040043

AMA Style

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. https://doi.org/10.3390/jimaging3040043

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. https://doi.org/10.3390/jimaging3040043

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