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Small-sample learning with salient-region detection and center neighbor loss for insect recognition in real-world complex scenarios

文献类型： 外文期刊

作者： Yang, Zhankui 1 ; Yang, Xinting 2 ; Li, Ming 2 ; Li, Wenyong 2 ;

作者机构： 1.Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100089, Peoples R China

3.Natl Engn Lab Qual & Safety Traceabil Technol & A, Beijing, Peoples R China

关键词： Fine-grained recognition; Small-sample learning; Salient-region detection; Center neighbor loss function

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2021 年 185 卷

页码：

收录情况： SCI

摘要： Most real-world scenarios face the problems of small-sample learning and fine-grained recognition. For many rare insect classes, collecting a large number of training samples is infeasible or even impossible. In contrast, humans are able to recognize a new object class with little supervision. This motivates us to address the problems of small-sample recognition and fine-grained recognition for insects by combining recognition and localization; this can provide an effective remedy for data scarcity and the two techniques can bootstrap from each other. In this paper, we propose a saliency-detection model to localize the key regions that have the largest discriminative features for fine-grained insect classification. The learner learns to predict foreground and background masks for such localization, having been trained on a training set annotated with bounding boxes. Additionally, to further generate discriminative features, a center neighbor loss function is used to construct a robust feature-space distribution. The proposed model is trained end-to-end on our small-sample learning dataset, which comprises 220 insect categories from a real-world complex environment. Compared with the method using prototypical networks, the proposed method achieves a superior performance, with a mean recognition rate (top-5 accuracy) of 57.65%, and can effectively recognize insects under small-sample and complex-scene conditions.

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