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Application of visual saliency and feature extraction algorithm applied in large-scale image classification

In recent years, content-based image retrieval method has attracted the attention of many researchers. With the emergence of large-scale computer vision data set, large-scale image classification is going to become an important challenge in the field of computer vision. The existing image classification algorithms mainly have the following defects: (1) lack of sufficient theoretical basis; (2) lower classification accuracy and larger error; (3) algorithm running time is too long because of large amount of data. In order to solve these problems, we introduce an algorithm based on visual saliency and feature extraction theory. Visual saliency means that intelligent algorithm can mark the significant areas in the picture through the simulation of human visual characteristics, and we can preliminarily screen the large-scale date set in terms of saliency extraction, in order to constitute an initial categories set. Image classification mainly includes three processes, that are feature extraction, computation of image pattern vector, classification. Feature extraction is the key factor that affects the performance of image classification. Comparing to global features of image with blur background, partial occlusion, and illumination changes, local features are mode adaptive. Traditional image classification algorithms only use single feature, thus these methods bring limitations. In this paper, we have a thorough research of image classification methods based on feature combination, and we also propose an algorithm based on multiple feature. At the same time, in order to solve the problem that traditional methods dealing with the whole image in a non-hierarchical way, we introduce the theory of visual saliency detection. The results obviously shows that efficient feature extraction combined with the theory of visual saliency are significant to image classification.