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

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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 http://hdl.handle.net/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

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