Supervised spectral feature learning for fine-grained classification in small data set

Abstract

Fine-grained image classification is a challenging task due to the small inter-class variance, the large intra-class difference, and the small training data. Traditional methods typically rely on large-scale training samples with annotated part annotations, making them costly and severely limiting their application area. In this paper, we propose an effective and weakly supervised fine-grained classification framework. In this framework, a discriminative class-specific spectral feature is learned by intra-class spectral coupling and inter-class spectral decoupling under the weak supervision of image-level category labels, and then the new input images are classified based on the learned class-specific spectral feature. Different from existing strong supervised methods, the proposed technique creatively combines weak supervision of the image-level category labels with unsupervised spectral graph decomposition, not relying on large-scale training samples with dense part annotations, which are heavily labor-consuming. The performance of the proposed methods has been verified on four kinds of typical datasets: the JAFFE dataset, the Yale database, the UCI-CMU face database, and the neural foramina dataset. The satisfactory classification results have been achieved by the proposed method in expression recognition on the JAFFE dataset (with a mean accuracy of 95.31%), face recognition on the Yale database (with a mean accuracy of 98.79%), object recognition on the UCI-CMU face database (with a mean accuracy of 96.96%), and disease grading on the neural foramina dataset (with a mean accuracy of 92.09%). Compared with most state-of-the-art methods, the proposed method has superior classification performance in the small data set.