New Fuzzy-Mass Based Features for Video Image Type Categorization

Due to the large variety of video type collections, it becomes difficult to achieve good text detection and recognition accuracy. We propose a new fuzzy-mass based method for classifying (categorizing) text frames from different types of video. For each frame of a video type, we formulate Fuzzy logic to identify straight and curved edge components from edge images. We then estimate mass locally and globally by drawing consecutive ellipses over edge images with respect to straight and curved edge components. Further, we extract features based on spatial proximity between centroid of classified straight/curved edge components and that of the whole image. This results local features. Next, the features are extracted for the whole image without ellipse drawing, which results in global features. The combination of both local and global features is then fed to an SVM classifier for video type classification. Experimental results on the proposed and existing classification methods show that the proposed classification outperforms the stat of art methods. Furthermore, experiments on before and after classification with several text detection and binarization methods show that the proposed classification is significant in improving text detection and recognition performance.