A Word Bag Recognition Algorithm Based on Mixture Region Segmentation

Digital advertising image recognition and data analysis have a wide application in precise advertising push, viewing behavior analysis, and business web search. This paper studys the recognition and classification of digital media advertising images by analyzing the actual characteristics of advertising images and utilizing image feature extraction technology. Aiming at the shortcomings of the traditional visual word bag based on spatial Pyramid matching algorithm, e.g., the harsh matching condition and the lack of deformation resistance, this paper uses Gaussian mixture model to achieve the adaptive spatial zoning, by introducing the spatial location information of the feature points of the visual word bag. It is shown that the deformation resistance ability and the recognition accuracy in the proposed algorithm can attain good results in the test of the standard database and the local advertising database.