A static video summarization method based on the sparse coding of features and representativeness of frames

Abstract

This paper presents a video summarization method that is specifically for the static summary of consumer videos. Considering that the consumer videos usually have unclear shot boundaries and many low-quality or meaningless frames, we propose a two-step approach where the first step skims a video and the second step performs content-aware clustering with keyframe selection. Specifically, the first step removes most of redundant frames that contain only little new information by employing the spectral clustering method with color histogram features. As a result, we obtain a condensed video that is shorter and has clearer temporal boundaries than the original. In the second step, we perform rough temporal segmentation and then apply refined clustering for each of the temporal segments, where each frame is represented by the sparse coding of SIFT features. The keyframe selection from each cluster is based on the measure of representativeness and visual quality of frames, where the representativeness is defined from the sparse coding and the visual quality is the combination of contrast, blur, and image skew measures. The problem of keyframe selection is to find the frames that have both representativeness and high quality, which is formulated as an optimization problem. Experiments on videos with various lengths show that the resulting summaries closely follow the important contents of videos.

Static and Dynamic Video Summarization

“…Considering that the consumer videos usually have unclear shot boundaries and many low quality or meaningless frames. Experiments on videos with various lengths show that the resulting summaries closely follow the important contents of videos [4]. In 2018, Antti E. Ainasoja, et.al, focuses on the popular keyframe-based approach for video summarization.…”

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

“…Jeoung et al proposed a technique for a static summary of consumer videos. They completed the process in two steps: first they skimmed the video and then performed content-aware clustering with keyframe selection [ 43 ]. Yoon et al proposed an approach based on learning principal person appearance [ 44 ].…”

Content Based Video Retrieval Systems with Local Features: A Survey

“…Video division at first portions the primary picture outline as the picture outline into some moving articles and after that it tracks the advancement of the moving items in the ensuing picture outlines. In the wake of portioning objects in each picture outline, these sectioned items have numerous applications, for example, observation, protest control, scene segment, and video retrieval [10]. Video is made by taking an arrangement of shots and forming them together utilizing indicated synthesis administrators.…”

“…A novel programmed recovery method for sight and sound information called negative pseudo-pertinence criticism [10]. It endeavors to take in a versatile closeness space via consequently nourishing back the preparation information, which is recognized dependent on a conventional likeness metric.…”