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Telefonica Research at TRECVID 2010 Content-Based Copy Detection

Ehsan Younessian, Xavier Anguera, Tomasz Adamek, Nuria Oliver
2010 TREC Video Retrieval Evaluation  
Adamek have been partially funded by the Torres Quevedo program.  ... 
dblp:conf/trecvid/YounessianAAO10 fatcat:fjahcf3so5aw3elv4ftedrqur4

Telefonica Research Content-Based Copy Detection TRECVID Submission

Xavier Anguera, Pere Obrador, Tomasz Adamek, David Marimon, Nuria Oliver
2009 TREC Video Retrieval Evaluation  
This notebook paper presents the systems presented by Telefonica Research within the MESH team for the task of Video copy detection in TRECVID 2009. We participated in the Video-only, Audio-only and Audio+Video tasks. Our main contribution is the combination (when possible) of audio and video features within the same system by using global features extracted both from the reference videos and the queries. We also experimented with SIFTbased search methods and are aiming at building a hybrid
more » ... ch system. This is our first participation year and results are far from optimal, but some of them indicate the potential of the presented systems.
dblp:conf/trecvid/AngueraOAMO09 fatcat:jdgy3syfzfdcvlpxph7o7yf2ti

TRECVid 2006 Experiments at Dublin City University

Markus Koskela, Peter Wilkins, Tomasz Adamek, Alan F. Smeaton, Noel E. O'Connor
2006 TREC Video Retrieval Evaluation  
In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2006. We submitted the following six automatic runs: • F A 1 DCU-Base 6: Baseline run using only ASR/MT text features. • F A 2 DCU-TextVisual 2: Run using text and visual features. • F A 2 DCU-TextVisMotion 5: Run using text, visual, and motion features. • F B 2 DCU-Visual-LSCOM 3: Text and visual features combined with concept detectors. • F B 2 DCU-LSCOM-Filters 4: Text, visual,
more » ... d motion features with concept detectors. • F B 2 DCU-LSCOM-2 1: Text, visual, motion, and concept detectors with negative concepts. The experiments were designed both to study the addition of motion features and separately constructed models for semantic concepts, to runs using only textual and visual features, as well as to establish a baseline for the manually-assisted search runs performed within the collaborative K-Space project and described in the corresponding TRECVid 2006 notebook paper. The results of the experiments indicate that the performance of automatic search can be improved with suitable concept models. This, however, is very topic-dependent and the questions of when to include such models and which concept models should be included, remain unanswered. Secondly, using motion features did not lead to performance improvement in our experiments. Finally, it was observed that our text features, despite displaying a rather poor performance overall, may still be useful even for generic search topics.
dblp:conf/trecvid/KoskelaWASO06 fatcat:yz2much67ne4zpf3utcnduilh4

Telefonica Research at TRECVID 2011 Content-Based Copy Detection

Xavier Anguera, Tomasz Adamek, Daru Xu, Juan Manuel Barrios
2011 TREC Video Retrieval Evaluation  
This notebook paper summarizes the algorithms behind Telefonica Research participation in the NIST-TRECVID 2011 evaluation on the Video Copy Detection task. This year we have focused on 1) Improving the image-based matching system to better process video files; 2) implemented and tested a novel audio local fingerprint; and 3) improved the multimodality fusion algorithm from last year. For this year we have submitted 4 runs in total, whose main characteristics are described below:
dblp:conf/trecvid/AngueraAXB11 fatcat:evwqqyk2urhvhhsdz3pthr55cm

MASK: Robust Local Features for Audio Fingerprinting

Xavier Anguera, Antonio Garzon, Tomasz Adamek
2012 2012 IEEE International Conference on Multimedia and Expo  
A † For the duration of this project Antonio Garzon was a visiting scholar from Universitat Pompeu Fabra ‡ Tomasz Adamek is currently with www.catchoom.com fingerprint has a high discriminatory power if  ... 
doi:10.1109/icme.2012.137 dblp:conf/icmcs/AngueraGA12 fatcat:n3xd6nv57jeetbehsh6xv2lphi

Inexpensive fusion methods for enhancing feature detection

Peter Wilkins, Tomasz Adamek, Noel E. O'Connor, Alan F. Smeaton
2007 Signal processing. Image communication  
Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical Machine Learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused
more » ... ether in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.
doi:10.1016/j.image.2007.05.012 fatcat:mm3q3o6nd5f55nezucj2fxiblu

Multimodal fusion for video copy detection

Xavier Anguera, Juan Manuel Barrios, Tomasz Adamek, Nuria Oliver
2011 Proceedings of the 19th ACM international conference on Multimedia - MM '11  
Content-based video copy detection algorithms (CBCD) focus on detecting video segments that are identical or transformed versions of segments in a known video. In recent years some systems have proposed the combination of orthogonal modalities (e.g. derived from audio and video) to improve detection performance, although not always achieving consistent results. In this paper we propose a fusion algorithm that is able to combine as many modalities as available at the decision level. The
more » ... is based on the weighted sum of the normalized scores, which are modified depending on how well they rank in each modality. This leads to a virtually parameter-free fusion algorithm. We performed several tests using 2010 TRECVID VCD datasets and obtain up to 46% relative improvement in min-NDCR while also improving the F1 metric on the fused results in comparison to just using the best single modality.
doi:10.1145/2072298.2071979 dblp:conf/mm/AngueraBAO11 fatcat:j2nodh7md5f4dfixiiutiorilq

TRECVid 2007 Experiments at Dublin City University

Peter Wilkins, Tomasz Adamek, Gareth J. F. Jones, Noel E. O'Connor, Alan F. Smeaton
2007 TREC Video Retrieval Evaluation  
In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2007. We submitted the following six automatic runs: • F A 1 DCU-TextOnly6 : Baseline run using only ASR/MT text features. • F A 1 DCU-ImgBaseline4 : Baseline visual expert only run, no ASR/MT used. Made use of query-time generation of retrieval expert coefficients for fusion. • F A 2 DCU-ImgOnlyEnt5 : Automatic generation of retrieval expert coefficients for fusion at index time. •
more » ... F A 2 DCU-imgOnlyEntHigh3 : Combination of coefficient generation which combined the coefficients generated by the query-time approach, and the index-time approach, with greater weight given to the index-time coefficient. • F A 2 DCU-imgOnlyEntAuto2 : As above, except that greater weight is given to the query-time coefficient that was generated. • F A 2 DCU-autoMixed1 : Query-time expert coefficient generation that used both visual and text experts.
dblp:conf/trecvid/WilkinsAJOS07 fatcat:kgcibmabzrd7xf6znqyzfsbo5u

Efficient contour-based shape representation and matching

Tomasz Adamek, Noel O'Connor
2003 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval - MIR '03  
This paper presents an efficient method for calculating the similarity between 2D closed shape contours. The proposed algorithm is invariant to translation, scale change and rotation. It can be used for database retrieval or for detecting regions with a particular shape in video sequences. The proposed algorithm is suitable for real-time applications. In the first stage of the algorithm, an ordered sequence of contour points approximating the shapes is extracted from the input binary images.
more » ... contours are translation and scale-size normalized, and small sets of the most likely starting points for both shapes are extracted. In the second stage, the starting points from both shapes are assigned into pairs and rotation alignment is performed. The dissimilarity measure is based on the geometrical distances between corresponding contour points. A fast sub-optimal method for solving the correspondence problem between contour points from two shapes is proposed. The dissimilarity measure is calculated for each pair of starting points. The lowest dissimilarity is taken as the final dissimilarity measure between two shapes. Three different experiments are carried out using the proposed approach: letter recognition using a web camera, our own simulation of Part B of the MPEG-7 core experiment "CE-Shape1" and detection of characters in cartoon video sequences. Results indicate that the proposed dissimilarity measure is aligned with human intuition.
doi:10.1145/973264.973287 dblp:conf/mir/AdamekO03 fatcat:s3q4i3xnsrfdtpbkn63zlkqheu

Inexpensive Fusion Methods for Enhancing Feature Detection

Peter Wilkins, Tomasz Adamek, Noel E. O'Connor, Alan F. Smeaton
2007 2007 International Workshop on Content-Based Multimedia Indexing  
Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical Machine Learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused
more » ... ether in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.
doi:10.1109/cbmi.2007.385400 dblp:conf/cbmi/WilkinsAOS07 fatcat:j7pcn7crzrgktfhm2rsnc2td2m

DARTs: Efficient scale-space extraction of DAISY keypoints

David Marimon, Arturo Bonnin, Tomasz Adamek, Roger Gimeno
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
Winder et al. [15, 14] have recently shown the superiority of the DAISY descriptor [12] in comparison to other widely extended descriptors such as SIFT [8] and SURF [1] . Motivated by those results, we present a novel algorithm that extracts viewpoint and illumination invariant keypoints and describes them with a particular implementation of a DAISY-like layout. We demonstrate how to efficiently compute the scale-space and re-use this information for the descriptor. Comparison to similar
more » ... hes such as SIFT and SURF show higher precision vs recall performance of the proposed method. Moreover, we dramatically reduce the computational cost by a factor of 6x and 3x, respectively. We also prove the use of the proposed method for computer vision applications. 978-1-4244-6983-3/10/$26.00 ©2010 IEEE
doi:10.1109/cvpr.2010.5539936 dblp:conf/cvpr/MarimonBAG10 fatcat:w4sh7nuyifh7xif3jpg35mdrgy

A Framework and User Interface for Automatic Region Based Segmentation Algorithms

Kevin McGuinness, Gordon Keenan, Tomasz Adamek, Noel E. O'Connor
2006 International Conference on Semantics and Digital Media Technologies  
In this paper we describe a framework and tool developed for running and evaluating automatic region based segmentation algorithms. The tool was designed to allow simple integration of existing and future segmentation algorithms, both single image based algorithms and those that operate on video data. Our framework supports plug-in segmenters, media decoders, and region-map codecs. We provide several sophisticated implementations of these plug-ins, including a video decoder capable of frame
more » ... rate decoding of a large variety of video formats, an image decoder which also handles a comprehensive collection of formats, and a efficient implementation of a region-map codec. The tool includes both a graphical user interface to allow users to browse, visually inspect, and evaluate the algorithm output, and a batch processing interface for segmentation of large data collections. The application allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding input and output, and the front end for visualization.
dblp:conf/samt/McGuinnessKAO06 fatcat:iochzdfnbbbudl4ctg3h7jv6pu

Consensus statement on a screening programme for the detection of early lung cancer in Poland

Witold Rzyman, Joanna Didkowska, Robert Dziedzic, Tomasz Grodzki, Tadeusz Orłowski, Edyta Szurowska, Renata Langfort, Wojciech Biernat, Dariusz Kowalski, Wojciech Dyszkiewicz, Tadeusz Jędrzejczyk, Tomasz Zdrojewski (+3 others)
2018 Advances in Respiratory Medicine  
Conflict of interest Witold Rzyman and Mariusz Adamek developed the concept and design of the Consensus statement on a screening programme for the detection of early lung cancer in Poland.  ... 
doi:10.5603/arm.2018.0009 pmid:29490422 fatcat:2e6wqc5wcncoxgx2jj6yx7tnsq

Word matching using single closed contours for indexing handwritten historical documents

Tomasz Adamek, Noel E. O'Connor, Alan F. Smeaton
2006 International Journal on Document Analysis and Recognition  
Effective indexing is crucial for providing convenient access to scanned versions of large collections of handwritten historical manuscripts. Since traditional handwriting recognizers based on Optical Character Recognition (OCR) do not perform well on historical documents, recently a holistic word recognition approach has gained in popularity as an attractive and more straightforward solution [1] . Such techniques attempt to recognize words based on scalar and profilebased features extracted
more » ... m whole word images. In this paper, we propose a new approach to holistic word recognition for historical handwritten manuscripts based on matching word contours instead of whole images or word profiles. The new method consists of robust extraction of closed word contours and the application of an elastic contour matching technique proposed originally for general shapes [2] . We demonstrate that contour-based descriptors can effectively capture intrinsic word features. Our experiments show a recognition accuracy of 83%, which considerably exceeds the performance of other systems reported in the literature.
doi:10.1007/s10032-006-0024-y fatcat:mrzoqwcvfnhgrhfbdm4vqpbscy

Towards Fully Automatic Image Segmentation Evaluation [chapter]

Lutz Goldmann, Tomasz Adamek, Peter Vajda, Mustafa Karaman, Roland Mörzinger, Eric Galmar, Thomas Sikora, Noel E. O'Connor, Thien Ha-Minh, Touradj Ebrahimi, Peter Schallauer, Benoit Huet
2008 Lecture Notes in Computer Science  
Spatial region (image) segmentation is a fundamental step for many computer vision applications. Although many methods have been proposed, less work has been done in developing suitable evaluation methodologies for comparing different approaches. The main problem of general purpose segmentation evaluation is the dilemma between objectivity and generality. Recently, figure ground segmentation evaluation has been proposed to solve this problem by defining an unambiguous ground truth using the
more » ... salient foreground object. Although the annotation of a single foreground object is less complex than the annotation of all regions within an image, it is still quite time consuming, especially for videos. A novel framework incorporating background subtraction for automatic ground truth generation and different foreground evaluation measures is proposed, that allows to effectively and efficiently evaluate the performance of image segmentation approaches. The experiments show that the objective measures are comparable to the subjective assessment and that there is only a slight difference between manually annotated and automatically generated ground truth.
doi:10.1007/978-3-540-88458-3_51 fatcat:fizslgphk5enldnuy2ehw3siue
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