Giant Panda Identification

The lack of automatic tools to identify giant panda makes it hard to keep track of and manage giant pandas in wildlife conservation missions. In this paper, we introduce a new Giant Panda Identification (GPID) task, which aims to identify each individual panda based on an image. Though related to th...

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Bibliographic Details
Published in:IEEE transactions on image processing 2021, Vol.30, p.2837-2849
Main Authors: Wang, Le, Ding, Rizhi, Zhai, Yuanhao, Zhang, Qilin, Tang, Wei, Zheng, Nanning, Hua, Gang
Format: Article
Language:English
Subjects:
Annotations
Convolution
Datasets
Detectors
Face recognition
Feature extraction
feature fusion
fine-grained recognition
Giant panda identification
Image classification
Pandas
patch detector
Task analysis
Visualization
Wildlife conservation
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Summary:The lack of automatic tools to identify giant panda makes it hard to keep track of and manage giant pandas in wildlife conservation missions. In this paper, we introduce a new Giant Panda Identification (GPID) task, which aims to identify each individual panda based on an image. Though related to the human re-identification and animal classification problem, GPID is extraordinarily challenging due to subtle visual differences between pandas and cluttered global information. In this paper, we propose a new benchmark dataset iPanda-50 for GPID. The iPanda-50 consists of 6, 874 images from 50 giant panda individuals, and is collected from panda streaming videos. We also introduce a new Feature-Fusion Network with Patch Detector (FFN-PD) for GPID. The proposed FFN-PD exploits the patch detector to detect discriminative local patches without using any part annotations or extra location sub-networks, and builds a hierarchical representation by fusing both global and local features to enhance the inter-layer patch feature interactions. Specifically, an attentional cross-channel pooling is embedded in the proposed FFN-PD to improve the identify-specific patch detectors. Experiments performed on the iPanda-50 datasets demonstrate the proposed FFN-PD significantly outperforms competing methods. Besides, experiments on other fine-grained recognition datasets ( i.e. , CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that the proposed FFN-PD outperforms existing state-of-the-art methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3055627