Open Access

ARTICLE

Rare Bird Sparse Recognition via Part-Based Gist Feature Fusion and Regularized Intraclass Dictionary Learning

Jixin Liu^1,*, Ning Sun^1,2, Xiaofei Li¹, Guang Han¹, Haigen Yang¹, Quansen Sun³

1 Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, PR China.
2 Herbert and Florence Irving Medical Center, Columbia University, New York, NY 10032, United States of America.
3 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.

* Corresponding Author: Jixin Liu. Email: .

Computers, Materials & Continua 2018, 55(3), 435-446. https://doi.org/ 10.3970/cmc.2018.02177

Abstract

Rare bird has long been considered an important in the field of airport security, biological conservation, environmental monitoring, and so on. With the development and popularization of IOT-based video surveillance, all day and weather unattended bird monitoring becomes possible. However, the current mainstream bird recognition methods are mostly based on deep learning. These will be appropriate for big data applications, but the training sample size for rare bird is usually very short. Therefore, this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning. There are two achievements in our work: (1) after the part localization with selective search, the gist feature of all bird image parts will be fused as data description; (2) the fused gist feature needs to be learned through our proposed intraclass dictionary learning with regularized K-singular value decomposition. According to above two innovations, the rare bird sparse recognition will be implemented by solving one l1-norm optimization. In the experiment with Caltech-UCSD Birds-200-2011 dataset, results show the proposed method can have better recognition performance than other SR methods for rare bird task with small sample size.

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