Browsing by Subject "TRECVID"

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    Bilkent University at TRECVID 2007
    (National Institute of Standards and Technology, 2007) Aksoy, Selim; Duygulu, Pınar; Aksoy, C.; Aydin, E.; Gunaydin, D.; Hadimli, K.; Koç L.; Olgun, Y.; Orhan, C.; Yakin G.
    We describe our fourth participation, that includes two high-level feature extraction runs, and one manual search run, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual information, consisting of color, texture and edge-based low-level features, was used.
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    Bilkent University Multimedia Database Group at TRECVID 2008
    (National Institute of Standards and Technology, 2008-11) Küçüktunç, Onur; Baştan, Muhammet; Güdükkbay, Uğur; Ulusoy, Özgür
    Bilkent University Multimedia Database Group (BILMDG) participated in two tasks at TRECVID 2008: content-based copy detection (CBCD) and high-level feature extraction (FE). Mostly MPEG-7 [1] visual features, which are also used as low-level features in our MPEG-7 compliant video database management system, are extracted for these tasks. This paper discusses our approaches in each task.
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    A relevance feedback technique for multimodal retrieval of news videos
    (IEEE, 2005-11) Aksoy, Selim; Çavuş Özge
    Content-based retrieval in news video databases has become an important task with the availability of large quantities of data in both public and proprietary archives. We describe a relevance feedback technique that captures the significance of different features at different spatial locations in an image. Spatial content is modeled by partitioning images into non-overlapping grid cells. Contributions of different features at different locations are modeled using weights defined for each feature in each grid cell. These weights are iteratively updated based on user's feedback in terms of positive and negative labeling of retrieval results. Given this labeling, the weight updating scheme uses the ratios of standard deviations of the distances between relevant and irrelevant images to the standard deviations of the distances between relevant images. The proposed technique is quantitatively and qualitatively evaluated using shots related to several sports from the news video collection of the TRECVID video retrieval evaluation where the weights could capture relative contributions of different features and spatial locations. © 2005 IEEE.
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    Systematic evaluation of face detection algorithms on news videos
    (IEEE, 2007) Acar, Can; Atlas, Arda; Çevik, Koray; Ölmez İsa; Ünlü, Mustafa; Özkan, Derya; Duygulu, Pınar
    People are the most important subjects in news videos and for proper retrieval of people images; face detection is a very crucial step. However, face detection and recognition in news videos is a very challenging task due to the huge irregularities and high noise level in the data. In addition to that, with different face detection algorithms, the number and the type of the faces may differ. In this study, in order to get the best performance from existing methods, systematic evaluation of these methods is performed. In the experiments, news videos from TRECVID 2006 data set are used and for evaluation four different face detection methods are chosen.