Dublin City University at CLEF 2004: experiments with the ImageCLEF St Andrew's collection

For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness

Visual Information Retrieval in Endoscopic Video Archives

In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between 10 and 12 June 201

Similarity-based virtual screening using 2D fingerprints

This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available

Divide and Fuse: A Re-ranking Approach for Person Re-identification

As re-ranking is a necessary procedure to boost person re-identification (re-ID) performance on large-scale datasets, the diversity of feature becomes crucial to person reID for its importance both on designing pedestrian descriptions and re-ranking based on feature fusion. However, in many circumstances, only one type of pedestrian feature is available. In this paper, we propose a "Divide and use" re-ranking framework for person re-ID. It exploits the diversity from different parts of a high-dimensional feature vector for fusion-based re-ranking, while no other features are accessible. Specifically, given an image, the extracted feature is divided into sub-features. Then the contextual information of each sub-feature is iteratively encoded into a new feature. Finally, the new features from the same image are fused into one vector for re-ranking. Experimental results on two person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, our method outperforms the state-of-the-art on the Market-1501 dataset.Comment: Accepted by BMVC201

Learning to detect video events from zero or very few video examples

In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a textual description of the event, without using any positive video examples, and ii) additionally exploiting very few positive training samples together with a small number of ``related'' videos. For learning only from an event's textual description, we first identify a general learning framework and then study the impact of different design choices for various stages of this framework. For additionally learning from example videos, when true positive training samples are scarce, we employ an extension of the Support Vector Machine that allows us to exploit ``related'' event videos by automatically introducing different weights for subsets of the videos in the overall training set. Experimental evaluations performed on the large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for publicatio

Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases

This paper reports an evaluation of both graph-based and fingerprint-based measures of structural similarity, when used for virtual screening of sets of 2D molecules drawn from the MDDR and ID Alert databases. The graph-based measures employ a new maximum common edge subgraph isomorphism algorithm, called RASCAL, with several similarity coefficients described previously for quantifying the similarity between pairs of graphs. The effectiveness of these graph-based searches is compared with that resulting from similarity searches using BCI, Daylight and Unity 2D fingerprints. Our results suggest that graph-based approaches provide an effective complement to existing fingerprint-based approaches to virtual screening

United we fall, divided we stand: A study of query segmentation and PRF for patent prior art search

Previous research in patent search has shown that reducing queries by extracting a few key terms is ineffective primarily because of the vocabulary mismatch between patent applications used as queries and existing patent documents. This finding has led to the use of full patent applications as queries in patent prior art search. In addition, standard information retrieval (IR) techniques such as query expansion (QE) do not work effectively with patent queries, principally because of the presence of noise terms in the massive queries. In this study, we take a new approach to QE for patent search. Text segmentation is used to decompose a patent query into selfcoherent sub-topic blocks. Each of these much shorted sub-topic blocks which is representative of a specific aspect or facet of the invention, is then used as a query to retrieve documents. Documents retrieved using the different resulting sub-queries or query streams are interleaved to construct a final ranked list. This technique can exploit the potential benefit of QE since the segmented queries are generally more focused and less ambiguous than the full patent query. Experiments on the CLEF-2010 IP prior-art search task show that the proposed method outperforms the retrieval effectiveness achieved when using a single full patent application text as the query, and also demonstrates the potential benefits of QE to alleviate the vocabulary mismatch problem in patent search