A SURVEY OF OBJECT VIDEO RETRIEVAL SYSTEMS USING SCALE-INVARIANT FEATURE TRANSFORM (SIFT)

Abstract

Multimedia information systems are increasingly important with the advent of broadband networks, high-powered
work stations, and compression standards. Since visual media requires large amounts of storage and processing, there is a
need to efficiently index, store, and retrieve the visual information from multimedia database. Content based video retrieval
is a proper solution to handle the video data. But because of their huge volumes and high dimensionality, finding a proper
way to organize them for efficient search and retrieval becomes a challenging and important task. Proposed work is to
retrieve video from the database by giving query as an object. Video is firstly converted into frames, these frames are then
segmented and an object is separated from the image. Then features are extracted from object image by using SIFT
algorithm. Features of the video database obtained by the segmentation and feature extraction using SIFT algorithm are
matched by Nearest Neighbor Search (NNS). Experimental Results confirm the effectiveness and robustness of the algorithm.