Latent Local Feature Extraction for Low-Resolution Virus Image Classification

Abstract

Virus image classification is a significant and challenging issue in both clinical virology and medical image processing. Due to the low-resolution virus images in the original dataset, there is tricky difficulty in extracting useful features from this kind of poor quality images adopting the traditional feature extraction methods. In this paper, we propose an effective and robust method, which eliminates the drawbacks of traditional local feature extraction methods and conducts latent local texture feature extraction thus to promote the accuracy of virus image classification. Firstly, the multi-scale principal component analysis (PCA) filters are learned from all original images. Then, it establishes a scale space for each PCA-filtered image by 2D Gaussian function. Finally, some typical feature descriptors are employed to extract texture features from all images, which include the original image and its filtered images by PCA and Gaussian filters. Aiming at the classification of low-resolution images, the proposed method solves the difficulty in extracting the essential feature from the original image and captures its latent and principal texture information from different perspectives in different filtered images. Experimental results show that the classification accuracy of the proposed method is much higher than state-of-the-art methods in the same low-resolution virus dataset, reaching 88.00%.