Scene classification is an important part of machine vision. The content of scene is identified by analyzing the objects in the scene and its relative position. In recent years

the amount of image surged has introduced great challenges in image recognition

retrieval

and classification. Accurately obtaining the information needed by users for processing vast data is becoming increasingly urgent in this field. Early image recognition technology has focused mainly on describing the low-level information of images. The bag-of-words model is applied in document processing. This model transforms the document to a combination of keywords first and then conducts matching on the basis of the frequency of keywords. In recent years

this method has been applied to image processing successfully by researchers in computer vision. The image is represented to the document in the bag-of-words model. The visual words of image can be generated by image feature extraction

and the bag-of-words of image can be completed on the basis of the frequency of visual words. At present

an ideal classification effect cannot be achieved easily because of the diversity and complexity of the internal structure of scene classification. Physiological and psychological research has shown that the human visual system pays more attention to significant regions than significant points

and these regions are referred to as saliency regions. Visual attention model is a new major topic in research. Saliency analysis finds the region with most interests and most content of the image by use of a certain calculation method and represents with a saliency figure. In this study

a scene classification algorithm based on visual perception is proposed to address the key and difficult problems in scene classification. Specifically

the visual perception characteristics of the human eyes are considered and significance detection combined with traditional bag-of-visual-words model is employed. On the basis of visual significance and phonetic model

this study fully considers the visual attention area of the human eye and avoids the shortcoming of simple low-level features of failing to capture the interrelationships among targets. On this basis

a multi-scale fusion WSSIFT feature is established using the prominence of the region of interest and the underlying characteristics of screening and weighting to avoid the neglect of important details and remove some of the redundant features. First

the image is decomposed in multi-scale and the image features at each scale are extracted. Second

the visual area of image is detected at each scale. Finally

significant region information and multi-scale feature are integrated to constitute the multi-scale fusion WSSIFT feature and classify scenes. The proposed algorithm is tested on three standard datasets

namely

SE

LS and IS

to verify its effectiveness

and the results are compared with those of different methods. The classification accuracy of the proposed method is improved by approximately 3%~17%. The proposed scene classification algorithm can effectively improve the limitation of simple feature description and the overall expression of image. This method is based on the simple use of image features in scene classification with insufficient feature extraction and neglected interrelation of each object in the scene. This method fully considers human visual perception. On the basis of preserving the advantages of the local subordinate characteristic model

the fusion detection algorithm is used to study the overall sensitivity of image in consideration of the interrelationship between the entire scene and the enhancement in the local information. Accordingly

the multi-scale fusion WSSIFT feature is constructed. Experimental results show that the proposed algorithm exerts good classification effect on multiple datasets. The results of the proposed method on three standard datasets are superior to those of other algorithms. The novel algorithm can be applied to other machine vision fields

such as analysis

understanding

and classification of scenes.