Optimized Hypergraph Based Social Image Search Using VisualTextual Joint Relevance Learning
Arya S · IOSR Journal of Computer Engineering · 2014
Recent years have witnessed a great success of social media websites.Tag-based image search is an important approach to access the image content of interest on these websites.However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or lacking in diversity.Most of the existing methods estimate the relevance of images by using tags and visual characteristics either separately or sequentially.The proposed system uses an approach that utilize simultaneously both visual information and textual information in real time to estimate the relevance of user tagged image.The method used to determine the relevance estimation is the hypergraph learning approach.The hypergraph is a generalization of a graph in which an edge in the hypergraph can be connected to any number of vertices.In the proposed method each social image can be represented by the bag-of-visual words and bag-of-textual words, which can be obtained from the textual content and visual content of the particular image.A hypergraph can be constructed in which the vertices represent the social images for ranking and the each hyperedge represents the visual words or tags that are obtained from the image.In the hypergraph learning scheme, both the visual content and tag information are taken into consideration at same time.Different from the method used by the traditional hypergraph, in the proposed system a social image hypergraph is constructed where vertices represent the images and hyperedges represent the visual or textual terms.The set of pseudo-positive images are used to achieve the learning, where the weight of hyperedges are updated throughout the learning process.Thus only the most relevant images are given to the user.