Pre-Processing for Fine-Grained Image Classification

Hao GE  Feng YANG  Xiaoguang TU  Mei XIE  Zheng MA  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D    No.8    pp.1938-1942
Publication Date: 2017/08/01
Publicized: 2017/05/12
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8076
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
fine-grained classification,  object detection,  neural network,  

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Recently, numerous methods have been proposed to tackle the problem of fine-grained image classification. However, rare of them focus on the pre-processing step of image alignment. In this paper, we propose a new pre-processing method with the aim of reducing the variance of objects among the same class. As a result, the variance of objects between different classes will be more significant. The proposed approach consists of four procedures. The “parts” of the objects are firstly located. After that, the rotation angle and the bounding box could be obtained based on the spatial relationship of the “parts”. Finally, all the images are resized to similar sizes. The objects in the images possess the properties of translation, scale and rotation invariance after processed by the proposed method. Experiments on the CUB-200-2011 and CUB-200-2010 datasets have demonstrated that the proposed method could boost the recognition performance by serving as a pre-processing step of several popular classification algorithms.