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Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

Authors:

C. SchmidAuthors Info & Claims

International Journal of Computer Vision, Volume 73, Issue 2

Pages 213 - 238

https://doi.org/10.1007/s11263-006-9794-4

Published: 21 June 2007 Publication History

Abstract

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover's Distance and the ² distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on four texture and five object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance via extensive tests on the PASCAL database, for which ground-truth object localization information is available. Our experiments demonstrate that image representations based on distributions of local features are surprisingly effective for classification of texture and object images under challenging real-world conditions, including significant intra-class variations and substantial background clutter.

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Index Terms

Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study
1. Computing methodologies

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cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 73, Issue 2

June 2007

114 pages

ISSN:0920-5691

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Copyright © Copyright © 2007 Springer Science + Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 21 June 2007

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Cited By

Lyra LFabris AFlorindo J(2024)A multilevel pooling scheme in convolutional neural networks for texture image recognitionApplied Soft Computing10.1016/j.asoc.2024.111282152:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111282
Pittaras NGiannakopoulos GStamatopoulos PKarkaletsis V(2023)Content-based and Knowledge-enriched Representations for Classification Across Modalities: A SurveyACM Computing Surveys10.1145/358368255:14s(1-40)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3583682
Zhang YChen NQi SXue MHua Z(2023)Detection of Recolored Image by Texture Features in Chrominance ComponentsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357107619:3(1-23)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3571076
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Lan SLiao XFan HHu SPan Z(2023)A multi-channel framework based Local Binary Pattern with two novel local feature descriptors for texture classificationDigital Signal Processing10.1016/j.dsp.2023.104124140:COnline publication date: 1-Aug-2023
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Boudra SYahiaoui IBehloul A(2022)Tree trunk texture classification using multi-scale statistical macro binary patterns and CNNApplied Soft Computing10.1016/j.asoc.2022.108473118:COnline publication date: 1-Mar-2022
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Zheng QGong MYou XTao D(2022)A Unified B-Spline Framework for Scale-Invariant Keypoint DetectionInternational Journal of Computer Vision10.1007/s11263-021-01568-3130:3(777-799)Online publication date: 1-Mar-2022
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Kumar SPradhan JPal AIslam SKhan M(2022)Radiological image retrieval technique using multi-resolution texture and shape featuresMultimedia Tools and Applications10.1007/s11042-021-10525-881:10(13633-13660)Online publication date: 1-Apr-2022
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