Image classification based on sparse coding multi-scale spatial latent semantic analysis

Abstract

Abstract In the face of huge amounts of image data, how to let the computer simulate human cognition of images and automatically classify images into different semantic categories have become a key issue in image semantic analysis. Image classification is based on some attribute of the image, and it is divided into pre-set categories. For human beings, image classification is not difficult but there is a series of problems in using computers to classify images: (1) images contain a large amount of information, which is complex, diverse, and indescribable; and (2) there is a huge difference between the physical expression of images and the conceptual information known by human beings. The traditional sparse coding method loses the spatial information when classifying images. In this paper, spatial pyramid multi-partition method is used to add spatial information restriction to the feature. The proposed multi-scale spatial latent semantic analysis method based on sparse coding has higher average classification accuracy than many existing methods, which verifies its effectiveness and robustness. Experiments also show that the classification accuracy of this paper is 2.1% higher than that of sparse coding for image classification (ScSPM) and the classification performance is 3.1% higher than that of ScSPM when the number of training images is 40. Compared with other methods, the classification performance of the proposed method is improved significantly.

Keywords

• Sparse coding
• Multi-scale space
• Latent semantic analysis
• Image classification
• Image segmentation