Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization

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 commo...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 23; no. 2; pp. 623 - 634
Main Authors Shenghua Gao, Tsang, Ivor Wai-Hung, Yi Ma
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.02.2014
Institute of Electrical and Electronics Engineers
Subjects
Applied sciences
Class-specific dictionary
Coding, codes
Dictionaries
Encoding
Exact sciences and technology
Feature extraction
fine-grained classification
Image coding
Image processing
Image reconstruction
Image representation
Information, signal and communications theory
Optimization
shared dictionary
Signal and communications theory
Signal processing
Telecommunications and information theory
Learning
Performance evaluation
Dictionaries
State of the art
fine-grained classification
Coding
Granular material
shared dictionary
Class-specific dictionary
Categorization
Event analysis
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2013.2290593