Fetching Relevant Images and Videos in Keyword Based Search Mechanism with Hypergraph Learning and Similarity Matching

This paper is an attempt for the work done for image and video retrieval system. Content-Based Image Retrieval(CBIR) and Content-Based Video Retrieval(CBVR), has attracted researchers from various research fields like, computer vision, artificial intelligence, human factors, machine learning, image processing, man-machine modeling to name a few. Existing methods have revealed certain flaws like noisy data, often leading to display of irrelevant images or videos. In our system we have used Hypergraph Learning for images and Similarity Matching for videos. All the above challenges are addressed by retrieving relevant images and videos in response to user's keyword based search approach. User can search by attributes present during search or user can search by new attribute which gets added in the attribute list in the database and we can have ranking of the retrieved results and get relevant data. Experimentation is carried out from Flickr database for images and videos under consideration are those which are available on YouTube. A database of images and videos that cover user's interest in diverse domains is designed and used in experimentation.