Browsing by Subject "News videos"
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Item Open Access Finding people frequently appearing in news(Springer, 2006-07) Özkan, Derya; Duygulu, PınarWe propose a graph based method to improve the performance of person queries in large news video collections. The method benefits from the multi-modal structure of videos and integrates text and face information. Using the idea that a person appears more frequently when his/her name is mentioned, we first use the speech transcript text to limit our search space for a query name. Then, we construct a similarity graph with nodes corresponding to all of the faces in the search space, and the edges corresponding to similarity of the faces. With the assumption that the images of the query name will be more similar to each other than to other images, the problem is then transformed into finding the densest component in the graph corresponding to the images of the query name. The same graph algorithm is applied for detecting and removing the faces of the anchorpeople in an unsupervised way. The experiments are conducted on 229 news videos provided by NIST for TRECVID 2004. The results show that proposed method outperforms the text only based methods and provides cues for recognition of faces on the large scale. © Springer-Verlag Berlin Heidelberg 2006.Item Open Access Improvement of face detection algorithms for news videos(IEEE, 2005) Ikizler, Nazlı; Duygulu, PınarPeople are the most important subjects in news videos and for proper retrieval of person 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. This study presents a method that combines skin detection and Schneiderman-Kanade face detection, for improving the face detection performance in news videos for a better retrieval. This method has been tested on TRECVID 2003 dataset and the results are very promising. © 2005 IEEE.Item Open Access MPEG-7 uyumlu video veri tabanlari için önemli nesnelerin otomatik olarak bulunmasi(IEEE, 2008-04) Baştan, Muhammed; Güdükbay, Uğur; Ulusoy, ÖzgürBu çalışma, genel olarak nesneye dayalı endekslemeyi destekleyen, özel olarak MPEG-7 uyumlu veritabanları için, videolardan önemli nesnelerin otomatik olarak çıkarılmasını saglayabilecek bir yöntem sunmaktadır. Şimdiye kadar yapılan benzer çalışmalar genellikle resimler üzerinde yoğunlaşmış ve sadece ilk bakışta dikkati çeken alanları bulmaya çalışmıştır. Önerilen yöntem ise videolar üzerinde çalışmak için tasarlanmış olup sadece ilk bakışta dikkat çeken bölgelerin değil, videonun endekslenmesi için önemli sayılabilecek bölgelerin de bulunabilmesini amaçlamaktadır. Bunun için önce video kareleri bölütlere ayrılmakta, sonra her bölüt için yerel ve genel renk, biçim, doku ve hareket bilgileri hesaplanmakta, son olarak bu özellikler kullanılarak eğitilmiş bir destek vektor makinesi (SVM) kullanılarak bölgelerin önemli olup olmadığına karar verilmektedir. İlk deney sonuçları önerilen y öntemin başarılı olduğunu ve elde edilen nesnelerin öncekilere g öre anlamsal olarak daha iyi olduğunu göstermektedir. We describe a method to automatically extract video objects, which are important for object-based indexing of videos in an MPEG-7 compliant video database system. Most of the existing salient object detection approaches detect visually conspicuous image structures, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of local and global color, shape, texture and motion features for each region. Finally, the regions are classified as being salient or non-salient using SVMs trained on a few hundreds of example regions. Experimental results from news video segments show that the proposed method is more effective in extracting the important regions in terms of human visual perception. ©2008 IEEE.Item Open Access Recognizing objects and scenes in news videos(Springer, 2006-07) Baştan, Muhammet; Duygulu, PınarWe propose a new approach to recognize objects and scenes in news videos motivated by the availability of large video collections. This approach considers the recognition problem as the translation of visual elements to words. The correspondences between visual elements and words are learned using the methods adapted from statistical machine translation and used to predict words for particular image regions (region naming), for entire images (auto-annotation), or to associate the automatically generated speech transcript text with the correct video frames (video alignment). Experimental results are presented on TRECVID 2004 data set, which consists of about 150 hours of news videos associated with manual annotations and speech transcript text. The results show that the retrieval performance can be improved by associating visual and textual elements. Also, extensive analysis of features are provided and a method to combine features are proposed. © Springer-Verlag Berlin Heidelberg 2006.Item Open Access A relevance feedback technique for multimodal retrieval of news videos(IEEE, 2005-11) Aksoy, Selim; Çavuş ÖzgeContent-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.Item Open Access Segmentation-based extraction of important objects from video for object-based indexing(IEEE, 2008-06) Baştan, Muhammet; Güdükbay, Uğur; Ulusoy, ÖzgürWe describe a method to automatically extract important video objects for object-based indexing. Most of the existing salient object detection approaches detect visually conspicuous structures in images, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of regional and inter-regional color, shape, texture and motion features for all regions, which are classified as being important or not using SVMs trained on a few hundreds of example regions. Finally, each important region is tracked within each shot for trajectory generation and consistency check. Experimental results from news video sequences show that the proposed approach is effective. © 2008 IEEE.Item Open Access Systematic evaluation of face detection algorithms on news videos(IEEE, 2007) Acar, Can; Atlas, Arda; Çevik, Koray; Ölmez İsa; Ünlü, Mustafa; Özkan, Derya; Duygulu, PınarPeople 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.