Scene classification based on gray level-gradient co-occurrence matrix in the neighborhood of interest points

Scene classification is an important application field of multimedia information technology, whereas how to extract features from image is one of the key technologies in scene classification and recognition. A new method of extracting features is presented in this paper, it extracts features through gray level-gradient co-occurrence matrix in the neighborhood of interest points, also it can reserve the key image edge information, and it is called GGNP for short in the paper. The weighted Gower's similarity coefficient model is adopted as the basis for image scene classification, as it is more flexible than Euclidean distance function. Compared with traditional methods, the method has a good invariance in image scaling, rotation, translation and robust across a substantial range of affine distortion, meanwhile having good real-time. Experimentations are designed to test the precision and time-consuming of the method, the results of experiments show that the method has good effects on scene classification.