Key Frame Extraction Based on Entropy Difference and Perceptual Hash

Abstract

Key frame extraction is a crucial step in content-based video retrieval. To accurately describe character of frames, various features like color, texture, shape can be integrated and used for key frame extraction. In this paper, we proposed an improved two-phase approach of key frame extraction based on entropy and perceptual hash. It weakens the threshold's direct influence on final results, and solves the problem of fading, sunlight and other information easily resulting in redundant key frames. Firstly, candidate key frames are selected with the use of golden-section partition and weighted histogram. Next, key frames are determined by the entropy values of candidate frames. Finally, a new method of perceptual hash is applied to remove redundant key frames. Experimental data set is created with videos from different domains like movie, cartoon, news etc. Results show that the proposed method is accurate and effective for key frame extraction. The selected key frames can be a good representative of main content.