Content based Video Retrieval using Latent Semantic Indexing and Color, Motion and Edge Features

Abstract

Optimal efficiency of the retrieval techniques depends on the search methodologies that are used in the video processing system. The use of inappropriate search methodologies may make the processing system ineffective. Hence, an effective video segmentation and retrieval system is an essential prerequisite for searching a relevant video from a huge collection of videos. In this paper we propose a video retrieval system based on the integration of various visual cues. In contrast to key-frame based representation of shot, our approach analyzes all frames within a shot to construct a compact representation of video shot. In feature extraction step we extract quantized color, motion and edge density features. A similarity measure is defined using LSI (Latent semantic indexing) to locate the occurrence of similar video clips in the database. Our approach is able to fully exploit the spatio-temporal contents of the video. Experimental results indicate that the proposed algorithm is effective and outperforms some existing technique. The detailed result analysis and graphs supports the effectiveness and correctness of the system. General Terms Image/Video Processing, Video Retrieval