ACM Computing Surveys (CSUR)
Content-based query of image databases: inspirations from text retrieval
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Content-Based Image and Video Retrieval
Content-Based Image and Video Retrieval
Representation of images for classification with independent features
Pattern Recognition Letters
Topographic Independent Component Analysis
Neural Computation
Locally Salient Feature Extraction Using ICA for Content-Based Face Image Retrieval
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
fMRI Brain Image Retrieval Based on ICA Components
ENC '07 Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science
Dimensional Reduction Based on Independent Component Analysis for Content Based Image Retrieval
JCAI '09 Proceedings of the 2009 International Joint Conference on Artificial Intelligence
A new multi-view learning algorithm based on ICA feature for image retrieval
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Medical image retrieval using texture, locality and colour
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Context-Aware features for singing voice detection in polyphonic music
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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This study aims to find more effective methods for collection-specific CBIR. A lot of work has been done in trying to adapt a system by user feedback, in this study we aim to adapt CBIR systems for specific image collections in an automated manner. Independent Component Analysis (ICA), a high order statistical technique, is used to extract Independent Component Filters (ICF) from image sets. As these filters are adapted to the data, the hypothesis is that they may provide features which are more effective for collection-specific CBIR. To test this question, this study develops a methodology to extract ICF from image sets and use them to extract filter responses. In developing this method, the study uses image cross-correlation and clustering to solve issues to do with shifted/duplicate filters and selecting a smaller set of filters to make CBIR practical. The method is used to generate filter responses for the VisTex database . The filter response energies are used as features in the GNU Image Finding Tool (GIFT). The experiments show that features extracted using ICF have the potential to improve the effectiveness of collection-specific CBIR, although some more work in this area is required.