Learning deep and sparse feature representation for fine-grained object recognition

M. Srinivas, Yen Yu Lin, Hong Yuan Mark Liao

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Scopus citations

Abstract

In this paper, we address fine-grained classification which is quite challenging due to high intra-class variations and subtle inter-class variations. Most modern approaches to fine-grained recognition are established based on convolutional neural networks (CNN). Despite the effectiveness, these approaches still suffer from two major problems. First, they highly rely on large sets of training data, but manually annotating numerous training data is expensive. Second, the learned feature presentations by these approaches are often of high dimensions, leading to less efficiency. To tackle the two problems, we present an approach where on-line dictionary learning is integrated into CNN. The dictionaries can be incrementally learned by leveraging a vast amount of weakly labeled data on the Internet. With these dictionaries, all the training and testing data can be sparsely represented. Our approach is evaluated and compared with the state-of-the-art approaches on the benchmark dataset, CUB-200-2011. The promising results demonstrate its superiority in both efficiency and accuracy.
Original languageAmerican English
Title of host publication2017 IEEE International Conference on Multimedia and Expo (ICME)
PublisherIEEE Computer Society
Pages1458-1463
Number of pages6
ISBN (Print)9781509060672
DOIs
StatePublished - 28 Aug 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo

Keywords

  • Dictionary learning
  • Fine-grained categorization
  • Part-based RCNN
  • Sparse representation

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