Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

Abstract

Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access is video summarization. Compression of data through extraction of keyframes is a solution to these challenges. A keyframe is a representative frame of the salient features of the video. The output frames must represent the original video in temporal order. The proposed research presents a method of keyframe extraction using the mean of consecutive k frames of video data. A sliding window of size k / 2 is employed to select the frame that matches the median entropy value of the sliding window. This is called the Median of Entropy of Mean Frames (MME) method. MME is mean-based keyframes selection using the median of the entropy of the sliding window. The method was tested for more than 500 videos of sign language gestures and showed satisfactory results.