Browsing by Subject "High-level feature extractions"
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Item Open Access Bilkent University at TRECVID 2005(National Institute of Standards and Technology, 2005-11) Aksoy, Selim; Avcı, Akın; Balçık, Erman; Çavuş, Özge; Duygulu, Pınar; Karaman, Zeynep; Kavak, Pınar; Kaynak, Cihan; Küçükayvaz, Emre; Öcalan, Çağdaş; Yıldız, PınarWe describe our second-time participation, that includes one high-level feature extraction run, and three manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edgebased low-level features and textual information consisted of the speech transcript provided in the collection. With the experience gained with our second-time participation, we are in the process of building a system for automatic classification and indexing of video archives.Item Open Access Bilkent university at TRECVID 2006(National Institute of Standards and Technology, 2006-11) Aksoy, Selim; Duygulu, Pınar; Akçay, Hüseyin Gökhan; Ataer, Esra; Baştan, Muhammet; Can, Tolga; Çavuş, Özge; Doǧgrusöz, Emel; Gökalp, Demir; Akaydın, Ateş; Akoǧlu, Leman; Angın, Pelin; Cinbiş, R. Gökberk; Gür, Tunay; Ünlü, MehmetWe describe our third participation, that includes one high-level feature extraction run, and two manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edge-based low-level features and textual information consisted of the speech transcript provided in the collection.Item Open Access 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.Item Open Access Bilkent University Multimedia Database Group at TRECVID 2008(National Institute of Standards and Technology, 2008-11) Küçüktunç, Onur; Baştan, Muhammet; Güdükkbay, Uğur; Ulusoy, ÖzgürBilkent 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.Item Open Access COST292 experimental framework for TRECVID 2006(National Institute of Standards and Technology, 2006) Ćalić J.; Krämer P.; Naci, U.; Vrochidis, S.; Aksoy, S.; Zhangk Q.; Benois-Pineau J.; Saracoglu, A.; Doulaverakis, C.; Jarina, R.; Campbell, N.; Mezaris V.; Kompatsiaris I.; Spyrou, E.; Koumoulos G.; Avrithis, Y.; Dalkilic, A.; Alatan, A.; Hanjalic, A.; Izquierdo, E.In this paper we give an overview of the four TRECVID tasks submitted by COST292, European network of institutions in the area of semantic multimodal analysis and retrieval of digital video media. Initially, we present shot boundary evaluation method based on results merged using a confidence measure. The two SB detectors user here are presented, one of the Technical University of Delft and one of the LaBRI, University of Bordeaux 1, followed by the description of the merging algorithm. The high-level feature extraction task comprises three separate systems. The first system, developed by the National Technical University of Athens (NTUA) utilises a set of MPEG-7 low-level descriptors and Latent Semantic Analysis to detect the features. The second system, developed by Bilkent University, uses a Bayesian classifier trained with a "bag of subregions" for each keyframe. The third system by the Middle East Technical University (METU) exploits textual information in the video using character recognition methodology. The system submitted to the search task is an interactive retrieval application developed by Queen Mary, University of London, University of Zilina and ITI from Thessaloniki, combining basic retrieval functionalities in various modalities (i.e. visual, audio, textual) with a user interface supporting the submission of queries using any combination of the available retrieval tools and the accumulation of relevant retrieval results over all queries submitted by a single user during a specified time interval. Finally, the rushes task submission comprises a video summarisation and browsing system specifically designed to intuitively and efficiently presents rushes material in video production environment. This system is a result of joint work of University of Bristol, Technical University of Delft and LaBRI, University of Bordeaux 1.Item Open Access The COST292 experimental framework for TRECVID 2007(National Institute of Standards and Technology, 2007) Zhang, Q.; Corvaglia, M.; Aksoy, Selim; Naci, U.; Adami, N.; Aginako, N.; Alatan, A.; Alexandre, L. A.; Almeida, P.; Avrithis, Y.; Benois-Pineau, J.; Chandramouli, K.; Damnjanovic, U.; Esen, E.; Goya, J.; Grzegorzek, M.; Hanjalic, A.; Izquierdo, E.; Jarina, R.; Kapsalas, P.; Kompatsiaris, I.; Kuba, M.; Leonardi, R.; Makris, L.; Mansencal, B.; Mezaris, V.; Moumtzidou, A.; Mylonas, P.; Nikolopoulos, S.; Piatrik, T.; Pinheiro, A. M. G.; Reljin, B.; Spyrou, E.; Tolias, G.; Vrochidis, S.; Yakın, G.; Zajic, G.In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features.