Interactive retrieval of video using pre-computed shot-shot similarities

A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision

A scalable and efficient content-based multimedia retrieval system

In this work the problem of content-based information retrieval is approached from a new perspective. We look at a probabilistic approach in CBIR from the angle of Bayesian networks. Our data structure serves to break two bottlenecks of retrieval performance: (1) high dimensionality of feature vectors and (2) poor mapping of raw features into highlevel content that a human understands (the semantic gap). We use the network structure instead of the feature space, and propose updating the higherlevel content description by utilising the relevance feedback obtained from the user. Strategies for display update for the next iteration are studied. A new approach for selecting the next display set is tied to our data structure

Monitoring user-system performance in interactive retrieval tasks

Monitoring user-system performance in interactive search is a challenging task. Traditional measures of retrieval evaluation, based on recall and precision, are not of any use in real time, for they require a priori knowledge of relevant documents. This paper shows how a Shannon entropy-based measure of user-system performance naturally falls in the framework of (interactive) probabilistic information retrieval. The value of entropy of the distribution of probability of relevance associated with the documents in the collection can be used to monitor search progress in live testing, to allow for example the system to select an optimal combination of search strategies. User profiling and tuning parameters of retrieval systems are other important applications

Relevance feedback in probabilistic multimedia retrieval

In this paper we explore a new view on data organisation and retrieval in a (multimedia) collection. We use probabilistic framework for indexing and interactive retrieval of the data, which enable to fill the semantic gap. Semi-automated experiments with TREC-2002 video collection showed that our approach is efficient and effective

Probabilistic Approaches to Video Retrieval: The Lowlands Team at TREC VID 2004

Contains fulltext : 228224.pdf (publisher's version ) (Open Access)TRECVID 200

Combining information sources for video retrieval

The previous video track results demonstrated that it is far from trivial to take advantage of multiple modalities for the video retrieval search task. For almost any query, results based on ASR transcripts have been better than any other run. This year’s main success in our runs is that a combination of ASR and visual performs better than either alone! In addition we experimented with dynamic shot models, combining topic examples, feature extraction and interactive search

Learning user queries in multimodal dissimilarity spaces

Different strategies to learn user semantic queries from dissimilarity representations of video audio-visual content are presented. When dealing with large corpora of videos documents, using a feature representation requires the online computation of distances between all documents and a query. Hence, a dissimilarity representation may be preferred because its offline computation speeds up the retrieval process. We show how distances related to visual and audio video features can directly be used to learn complex concepts from a set of positive and negative examples provided by the user. Based on the idea of dissimilarity spaces, we derive three algorithms to fuse modalities and therefore to enhance the precision of retrieval results. The evaluation of our technique is performed on artificial data and on the complete annotated TRECVID corpus