Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82155
Title: Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization
Authors: Tsang, Ivor Wai-Hung
Gao, Shenghua
Ma, Yi
Keywords: Class-specific dictionary
Shared dictionary
Issue Date: 2014
Source: Gao, S., Tsang, I. W.-H., & Ma, Y. (2014). Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization. IEEE Transactions on Image Processing, 23(2), 623-634.
Series/Report no.: IEEE Transactions on Image Processing
Abstract: This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.
URI: https://hdl.handle.net/10356/82155
http://hdl.handle.net/10220/41143
ISSN: 1057-7149
DOI: 10.1109/TIP.2013.2290593
Rights: © 2013 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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