Automatic extraction of important objects for an MPEG-7 compliant video database system [MPEG-7 uyumlu vi̇deo veri̇ tabanlari i̇çi̇n önemli̇ nesneleri̇n otomati̇k olarak bulunmasi]
2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/26840
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.
- Conference Paper 
Showing items related by title, author, creator and subject.
Gerek O.N.; Altunbasak, Y. (1997)This paper describes a method for selecting key frames by using a number of parameters extracted from the MPEG video stream. The parameters are directly extracted from the compressed video stream without decompression. A ...
Aksoy, S.; Çavuş Ö. (2005)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 ...
Demirtas, K.; Cicekli, N.K.; Cicekli I. (2010)In this paper, we propose automatic categorization and summarization of documentaries using subtitles of videos. We propose two methods for video categorization. The first makes unsupervised categorization by applying ...