| A. Doulamis, N. Doulamis and S. Kollias |
| Relevance Feedback for Content-Based Retrieval in Video Databases: A Neural Network Approach |
| Proc of IEEE International Conference on Electronics, Circuits and Systems (ICECS), Paphos, Cyprus, Vol. 3, pp. 1745-1748 |
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ABSTRACT
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| 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, able for providing an efficient representation of the video content. For this reason, a cross correlation criterion is minimized using a genetic algorithm. Low level features are extracted to indicate the frame characteristics, such as color and motion segments. After the key frame extraction, the video queries are implemented directly on this small number of frames. To reduce, however, the limitation of lowlevel 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.
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| 01 January , 1999 |
| A. Doulamis, N. Doulamis and S. Kollias, "Relevance Feedback for Content-Based Retrieval in Video Databases: A Neural Network Approach", Proc of IEEE International Conference on Electronics, Circuits and Systems (ICECS), Paphos, Cyprus, Vol. 3, pp. 1745-1748 |
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