Neural network approach to interactive content-based retrieval of video databases

Doulamis Nikolaos, D; Doulamis Anastasios, D; Kollias Stefanos, D

dc.contributor.author	Doulamis Nikolaos, D	en
dc.contributor.author	Doulamis Anastasios, D	en
dc.contributor.author	Kollias Stefanos, D	en
dc.date.accessioned	2014-03-01T02:41:34Z
dc.date.available	2014-03-01T02:41:34Z
dc.date.issued	1999	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30537
dc.subject	Content Based Retrieval	en
dc.subject	Cross Correlation	en
dc.subject	Motion Segmentation	en
dc.subject	Video Database	en
dc.subject	Video Indexing	en
dc.subject	Feedforward Neural Network	en
dc.subject	Neural Network	en
dc.subject.other	Color image processing	en
dc.subject.other	Database systems	en
dc.subject.other	Feature extraction	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Indexing (of information)	en
dc.subject.other	Information retrieval	en
dc.subject.other	Learning systems	en
dc.subject.other	Optical correlation	en
dc.subject.other	Cross-correlation criterion	en
dc.subject.other	Video databases	en
dc.subject.other	Image analysis	en
dc.title	Neural network approach to interactive content-based retrieval of video databases	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.1999.822866	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.1999.822866	en
heal.publicationDate	1999	en
heal.abstract	A neural network scheme is presented in this paper for adaptive video indexing and retrieval. First, a limited but characteristic amount of frames are extracted from each video scene, by minimizing a cross-correlation criterion. Low level features are extracted to indicate the frame characteristics, such as color and motion segments. This is due to the fact that extraction of high-level, semantic, features from any kind of images is too hard to be implemented. After the key frame extraction, the video queries are implemented directly on this small number of frames. To reduce, however, the limitation of low-level features, the human is considered as a part of the process, meaning that he/she is able to assign a degree of appropriateness for each retrieved image of the system and then restart the searching. A feedforward neural network structure is proposed as a parametric distance for the retrieval, mainly due to the highly non linear capabilities. An adaptation mechanism is also proposed for updating the network weights, each time a new image selection is performed by the user. This mechanism can modify the network weights so that the output of the network, after the adaptation, is as much as close to the user's selection while simultaneously performing a minimal degradation of the previous learned data.	en
heal.publisher	IEEE, Los Alamitos, CA, United States	en
heal.journalName	IEEE International Conference on Image Processing	en
dc.identifier.doi	10.1109/ICIP.1999.822866	en
dc.identifier.volume	2	en
dc.identifier.spage	116	en
dc.identifier.epage	120	en