Video learning based image classification method for object recognition

Automatic image classification is the first step toward semantic understanding of an object in the computer vision area. The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream. In order to solve these problems, an effective and rapid image classification method was presented for the object recognition based on the video learning technique. The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence. After the selection of scene images, the local maximum points on corner of object around local area were found using the Harris corner detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform (SIFT) for extracting local descriptor. Finally, the extracted local descriptor was learned to the three-dimensional pyramid match kernel. Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.