WELL-REFINED SCHEME BY VISUAL INFORMATION

A hypergraph will be accustomed to model the connection between images by integrating low-level visual features and attribute features. Hypergraph ranking will be carried out to buy the pictures. Its fundamental principle is the fact that aesthetically similar images must have similar ranking scores. Image search Re-Ranking is an efficient method of refine the written text-based image Google listing. Most existing Re-Ranking approaches derive from low-level visual features. Within this paper, we advise to take advantage of semantic characteristics for image search Re-Ranking. In line with the classifiers for the predefined characteristics, each image is symbolized by a characteristic feature composed from the reactions from all of these classifiers. Within this work, we advise a visible-attribute joint hypergraph learning method of concurrently explore two information sources. We conduct experiments on greater than 1,000 queries in MSRA-MM V2. Dataset. The experimental results demonstrate the potency of our approach a hypergraph is built to model the connection of images

Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View

Multimedia collections are more than ever growing in size and diversity. Effective multimedia retrieval systems are thus critical to access these datasets from the end-user perspective and in a scalable way. We are interested in repositories of image/text multimedia objects and we study multimodal information fusion techniques in the context of content based multimedia information retrieval. We focus on graph based methods which have proven to provide state-of-the-art performances. We particularly examine two of such methods : cross-media similarities and random walk based scores. From a theoretical viewpoint, we propose a unifying graph based framework which encompasses the two aforementioned approaches. Our proposal allows us to highlight the core features one should consider when using a graph based technique for the combination of visual and textual information. We compare cross-media and random walk based results using three different real-world datasets. From a practical standpoint, our extended empirical analysis allow us to provide insights and guidelines about the use of graph based methods for multimodal information fusion in content based multimedia information retrieval.Comment: An extended version of the paper: Visual and Textual Information Fusion in Multimedia Retrieval using Semantic Filtering and Graph based Methods, by J. Ah-Pine, G. Csurka and S. Clinchant, submitted to ACM Transactions on Information System