Selective Pooling Vector for Fine-Grained Recognition
WACV(2015)
摘要
We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling Vector over the previous state-of-the-art Super Vector and Fisher Vector representations, is that it ensures a more accurate learning function, which proves to be important for classifying details in fine-grained image recognition. Our experimental results corroborate this claim: with a simple linear SVM as the classifier, the selective pooling vector achieves significant performance gains on standard benchmark datasets for various fine-grained tasks such as the CMU Multi-PIE dataset for face recognition, the Caltech-UCSD Bird dataset and the Stanford Dogs dataset for fine-grained object categorization. On all datasets we outperform the state of the arts and boost the recognition rates to 96.4%, 48.9%, 52.0% respectively.
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关键词
image representation,fine-grained object categorization,super vector representations,fine-grained image classification tasks,image recognition,cmu multipie dataset,linear svm classifier,image classification,selective local visual descriptor pooling,stanford dogs dataset,object detection,caltech-ucsd bird dataset,learning function,image recognition framework,selective pooling vector,fisher vector representations,support vector machines,fine-grained image recognition,visualization,vectors,encoding,face recognition
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