KEG, KIZI VŠE Praha, 18.12.2008

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Pavel Praks @ IEEE CBMI 2008 IEEE ICIP 2008 NIST TrecVid 2008 KEG, KIZI VŠE Praha, 18.12.2008

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KEG, KIZI VŠE Praha, 18.12.2008. Pavel Praks @ IEEE CBMI 2008 IEEE ICIP 2008 NIST TrecVid 2008. CBMI-2008 Sixth International Workshop on Content-Based Multimedia Indexing 18-20th June, 2008, London, UK. ~ 90 papers - PowerPoint PPT Presentation

Transcript of KEG, KIZI VŠE Praha, 18.12.2008

  • KEG, KIZI VE Praha, 18.12.2008Pavel Praks @IEEE CBMI 2008IEEE ICIP 2008NIST TrecVid 2008

  • CBMI-2008 Sixth International Workshop on Content-Based Multimedia Indexing 18-20th June, 2008, London, UK ~ 90 papers Interesting paper: Web-scale System for Image Similarity Search: When the Dreams are Coming True David Novak, Michal Batko and Pavel ZezulaProject MUFIN:

  • CBMI 08 Demo: Integration of an image retrieval Latent Semantic Indexing tool to a web serviceP. Praks (UEP) and K. Chandramouli (QMUL)The aim of the demonstration is to show our experience with an integration of a LSI-based image retrieval algorithm, which has been coded in Matlab, to a web-based multimedia retrieval framework. K-Space Content Retrieval System

  • LINEAR ALGEBRA FOR VISION-BASED SURVEILLANCE IN HEAVY INDUSTRY -CONVERGENCE BEHAVIOR CASE STUDY2VB Technical University of Ostrava, Ostrava, Czech Republic; Faculty of Electrical Engineering and Computer Science K-Space NoE, www.k-space.euPavel Praks1,2, Vojtch Svtek1, Jindich ernohorsk2 1University of Economics, Prague, Dept. of Information and Knowledge Engineering, Prague, Czech RepublicCBMI 2008, QMUL

  • Description of workThe surveillance application aims at improving the quality of technology via modelling human expert behaviour in the coking plant ArcelorMittal Ostrava, the Czech Republic. Video data on several industrial processes are captured by means of a CCD camera and classified by using Latent Semantic Indexing (LSI) with the respect to etalons classified by an expert.We also study the convergence behavior of proposed partial eigenproblem-based dimension reduction technique and its ability for knowledge acquisition.

  • Case studies of images taken from the coking plant ArcelorMittal Ostrava, CZSubjective evaluation of LSI-based results related to these two different settings: Experiment 1: k = 8 largest singular values is assumedExperiment 2: k = 45 largest singular values were used for LSI. For each case, query image represents a different industrial process. Image retrieval results are presented by decreasing order of similarity. The query image is situated in the upper left corner. (The similarity of the query image and the retrieved image is written in parentheses.) In order to achieve well arranged results, only 9 most significant images are presented.

  • LSI Image retrieval results: Case D, k=8The query image includes the detailed view of coke. All of the 8 most similar images are related to the same topic.

  • LSI Image retrieval results: Case D, k=45The retrieved images are not related to the same topic at all.

  • Properties of LSI Image retrieval

  • 2008 IEEE International Conferenceon Image Processing October 1215, 2008 San Diego, California, U.S.A.~ 800 papers (!!) papers:Vacura M., Svatek V., Saathoff C., Franz T., Troncy R.: Describing Low-Level Image Features Using The COMM Ontology. Maleki A., Shahram M., Carlsson G.: A Near Optimal Coder For Image Geometry With Adaptive Partitioning. (Stanford University, (wedgelets))

  • P. Praks, E. Izquierdo, R. Kuera: The sparse image representation for automated image retrieval. IEEE ICIP 2008. We keep the memory limit of the decomposed data by a statistical model of the sparse data. The effectiveness of our novel approach is demonstrated by the large scale image similarity task of the NIST TrecVid 2007 benchmark.

  • NIST TREC Video Retrieval Evaluation Online Proceedings17-18 November 2008, Gaithersburg, Maryland, USA. @ K-Space team:P. Wilkins, D. Byrne, Gareth J.F.Jones, H. Lee, G. Keenan, K. McGuinness, N. E. O'Connor, N. O'Hare, A. F. Smeaton, T. Adamek, R. Troncy, A. Amin, R. Benmokhtar, E. Dumont, B. Huet, B. Merialdo, G. Tolias, E. Spyrou, Y. Avrithis, G. Th. Papadopoulous, V. Mezaris, I. Kompatsiaris, R. Mrzinger, P. Schallauer, W. Bailer, K. Chandramouli, E. Izquierdo, L. Goldmann, M. Haller, A. Samour, A. Cobet, T. Sikora, P. Praks, D. Hannah, M. Halvey, F. Hopfgartner, R. Villa, P. Punitha, A. Goyal, J. M. Jose, "K-Space at TRECVid 2008", TREC Video Retrieval Evaluation, November, 2008

  • Vzkum ve spoluprci s prof. Pierre-Etienne LABEAU, Service de Mtrologie Nuclaire, Universit Libre de Bruxelles, Brusel, Belgie.Vstupem vyvjenho software jsou doby do poruchy (+ "stopping times" pro cenzorovan data). Implementovny 3 ekonomick modely popisujc rzn strategie drby (ABAO, AGAN, BAGAN). Populrn Weibull-modely nejsou vdy vhodn pro aproximaci vanov kivky:P. Praks @ Universit Libre de Bruxelles: Modelovn degradace Weibullovm rozdlenm

  • Dkuji Vm za pozornost!