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.
Highlights
With the rapid development of high-speed Internet, the development of information storage and transmission technology, and the popularity of digital equipment, the acquisition and storage of digital color images become easier and the number of image data people come into contact with is increasing at an unprecedented rate
The methods of image representation and classification by vector quantization encoding have the following problems: (1) visual codebook generated by vector quantization encoding will lead to the loss of spatial information, (2) visual vocabulary histogram construction leads to serious quantization errors, and (3) this kind of image representation will show good classification effect only when using non-linear kernel Support vector machine (SVM) classifier
The results fully show that the latent semantic information obtained by learning the Probabilistic latent semantic analysis (PLSA) model in each local area can improve the classification accuracy of images and verify the importance of PLSA in the image classification model in this paper
Summary
With the rapid development of high-speed Internet, the development of information storage and transmission technology, and the popularity of digital equipment, the acquisition and storage of digital color images become easier and the number of image data people come into contact with is increasing at an unprecedented rate. Faced with a huge amount of image data, how to simulate the cognitive mechanism of human understanding of images and automatically classify images into different semantic categories according to the way people understand become a key issue. According to the different ways of describing images in traditional image classification methods, image classification methods can be divided into two categories, one is based on global features and the other is based on middle-level semantic information. The methods of image representation and classification by vector quantization encoding have the following problems: (1) visual codebook generated by vector quantization encoding will lead to the loss of spatial information, (2) visual vocabulary histogram construction leads to serious quantization errors, and (3) this kind of image representation will show good classification effect only when using non-linear kernel SVM classifier.
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