Journal
IMAGE AND VISION COMPUTING
Volume 93, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.imavis.2019.10.006
Keywords
Fine-Grained Image Retrieval; CNN; Piecewise cross entropy loss
Categories
Funding
- National Natural Science Foundation of China [61503084, U1501251]
- Natural Science Foundation of Guangdong Province, China [2016A030310348]
- Science and Technology Program of Guangzhou, China [201804010098]
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Fine-Grained Image Retrieval is an important problem in computer vision. It is more challenging than the task of content-based image retrieval because it has small diversity within the different classes but large diversity in the same class. Recently, the cross entropy loss can be utilized to make Convolutional Neural Network (CNN) generate distinguish feature for Fine-Grained Image Retrieval, and it can obtain further improvement with some extra operations, such as Normalize-Scale layer. In this paper, we propose a variant of the cross entropy loss, named Piecewise Cross Entropy loss function, for enhancing model generalization and promoting the retrieval performance. Besides, the Piecewise Cross Entropy loss is easy to implement. We evaluate the performance of the proposed scheme on two standard fine-grained retrieval benchmarks, and obtain significant improvements over the state-of-the-art, with 11.8% and 3.3% over the previous work on CARS196 and CUB-200-2011, respectively. (C) 2019 Published by Elsevier B.V. reserved.
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