抽象的な

Video Retrieval Using Fusion of Visual Features and Latent Semantic Indexing

Rashmi M, Roshan Fernandes

It is commonly acknowledged that ever-increasing video databases should be efficiently indexed to facilitate fast video retrieval. Categorizing web-based videos is an important yet challenging task. The difficulties arise from large data diversity within a category, lack of labelled data etc. Similarity matching algorithm plays an important role in Video Retrieval System. Most of the video retrieval systems are designed using traditional similarity matching algorithms that are based on distance measures. The Accuracy of retrieval system depends on the method used for detecting shots, kind of video features used for retrieval. Semantic video indexing is a step towards automatic video indexing and retrieval. Here a Latent semantic indexing (LSI) technique, based on Singular Value Decomposition and fusion of visual features like color and edge is proposed for video indexing and retrieval. A key feature of LSI is its ability to establish associations between similar kinds of information, so the probability of producing accurate index is very high.