Skip to main content

Deep dictionary learning for fine-grained image classification

3Citations
Citations of this article
16Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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 challenges, we propose a model for fine-grained image classification with its application to bird species recognition. Based on the features extracted by bilinear convolutional neural network (BCNN), we propose an on-line dictionary learning 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 represented 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.

Cite

CITATION STYLE

APA

Srinivas, M., Lin, Y. Y., & Liao, H. Y. M. (2018). Deep dictionary learning for fine-grained image classification. In Proceedings - International Conference on Image Processing, ICIP (Vol. 2017-September, pp. 835–839). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296398

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free