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Deep Graph Laplacian Hashing for Image Retrieval Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Multi-scale Discriminative Patches for Fined-Grained Visual Categorization

Authors : Wenbo Tang, Hongxun Yao, Xiaoshuai Sun, Wei Yu

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Fine-grained visual categorization (FGVC) is a challenging vision problem since the similar appearance between object classes. It is important to note that human visual recognition system generally focuses on the specific part to distinguish those confused classes, which is also the breakthrough point for FGVC. In this paper, we will introduce the feedback mechanism of CNN to extract multi-scale discriminative patches. The extracted patches show more significance than the whole object region. Compared with tradition methods, we only require the object-level label rather than part-level annotations. Experiments on Caltech-UCSD Birds-200-2011 demonstrate the effectiveness of our method in solving FGVC.

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Metadata
Title
Multi-scale Discriminative Patches for Fined-Grained Visual Categorization
Authors
Wenbo Tang
Hongxun Yao
Xiaoshuai Sun
Wei Yu
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77380-3_68