Shrinking Encoding with Two-Level Codebook Learning for Fine-Grained Fish Recognition

Abstract

Bag-of-features (BoF) shows a great power in representing images for image classification. Many codebook learning methods have been developed to find discriminative parts of images for fine-grained recognition. Built upon BoF framework, we propose a novel approach for finegrained fish recognition with two-level codebook learning by shrinking coding coefficients. In the framework, only the maximum-valued coefficient will be maintained in the local spatial region if followed by max pooling strategy. However, the maximum-valued coefficient may result from a local descriptor which is not discriminative among fine-grained classes, resulting in difficulty in classification. In this paper, a two-level codebook is learned to represent the importance between the local descriptor and each codeword in its corresponding k-nearest neighbors. A shrinkage function is also introduced to shrink unrelated coefficients after encoding. Our experimental results show that the proposed method achieves significant performance improvement for fine-grained fish recognition tasks.