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

Combining Visible and Near-Infrared Cues for Image Categorisation

Standard digital cameras are sensitive to radiation in the near-infrared domain, but this additional cue is in general discarded. In this paper, we consider the scene categorisation problem in the context of images where both standard visible RGB channels and near infrared information are available. Using efficient local patch-based Fisher Vector image representations, we show based on thorough experimental studies the benefit of using this new type of data. We investigate which image descriptors are relevant, and how to best combine them. In particular, our experiments show that when combining texture and colour information, computed on visible and near-infrared channels, late fusion is the best performing strategy and outperforms the state-of-the-art categorisation methods on RGB-only data