Deep dictionary learning for fine-grained image classification

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

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

13 Scopus citations


Fine-grained image classification is quite challenging due to high inter-class similarity and large intra-class variations. Another issue is the small amount of training images with a large number of classes to be identified. To address the chal-lenges, we propose a model for fine-grained image classifi-cation with its application to bird species recognition. Based on the features extracted by bilinear convolutional neural network (BCNN), we propose an on-line dictionary learn-ing algorithm where the principle of sparsity is integrated into classification. The features extracted by BCNN encode pairwise neuron interaction in a translation-invariant manner. This property is valuable to fine-grained classification. The proposed algorithm for dictionary learning further carries out sparsity based classification, where training data can be rep-resented with a less number of dictionary atoms. It alleviates the problems caused by insufficient training data, and makes classification much more efficient. Our approach is evaluated and compared with the state-of-the-art approaches on the CUB-200-2011 dataset. The promising experimental results demonstrate its efficacy and superiority.
Original languageAmerican English
Title of host publication2017 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE Computer Society
Number of pages5
ISBN (Print)9781509021758
StatePublished - 20 Feb 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP


  • Deep learning
  • Fine-grained image classification
  • On-line dictionary learning
  • Sparse representation


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