Conference paper
Independent component analysis for understanding multimedia content
Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval classification rates increase if based on multimedia components relative to single media analysis.
For this purpose a simple probabilistic supervised classifier which works from unsupervised ICA features is invoked. In addition, we demonstrate the suggested framework for automatic annotation of descriptive key words to images.
| Language: | English |
|---|---|
| Publisher: | IEEE Press |
| Year: | 2002 |
| Pages: | 757-766 |
| Proceedings: | 2002 IEEE Workshop on Neural Networks for Signal Processing XII |
| ISBN: | 0780376161 and 9780780376168 |
| Types: | Conference paper |
| DOI: | 10.1109/NNSP.2002.1030096 |
| ORCIDs: | Hansen, Lars Kai , Larsen, Jan and Winther, Ole |
Content based retrieval Feature extraction Image analysis Image retrieval Independent component analysis Informatics Information retrieval Large scale integration Search engines Web pages automatic annotation content-based retrieval context dependent content descriptive key words feature extraction image classification image data image retrieval independent component analysis multimedia content multimedia databases probabilistic supervised classifier probability retrieval applications retrieval classification rates search applications text data unsupervised ICA features
