Unsupervised Video Shot Segmentation Using Global Color and Texture Information

Abstract

This paper presents an effective algorithm to segment color video into shots for video indexing or retrieval applications. This work adds global texture information to our previous work, which extended the scale-invariant feature transform (SIFT) to color global texture SIFT (CGSIFT). Fibonacci lattice-quantization is used to quantize the image and extract five color features for each region of the image using a symmetrical template. Then, in each region of the image partitioned by the template, the entropy and energy of a co-occurrence matrix are calculated as the texture features. With these global color and texture features, we adopt clustering ensembles to segment video shots. Experimental results show that the additional texture features allow the proposed CGTSIFT algorithm to outperform our previous work, fuzzy-c means, and SOM-based shot detection methods.