Boosting of Content Based Image Retrieval System |
Author(s): |
| Meenakshi , A.I.T. AJMER; Ruby Panwar, A.I.T. AJMER; Amit Kumar, A.I.T. AJMER |
Keywords: |
| Adaboost, Query learning, Precision and Recall, Strong Classifier, Weak Classifier |
Abstract |
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We present an approach for image retrieval using a very large number of highly selective features. Our approach is predicated on the assumption that each image is generated by a sparse set of visual “causes†and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure. At query time a user selects a few test images, and a technique known as “boosting†is used to learn a classification function in this feature space. By construction, the boosting procedure learns a simple classifier which only relies on 15 of the features. As a result a very large database of images can be scanned rapidly. Finally we will describe a set of experiments performed using our retrieval system on a Caltech database. |
Other Details |
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Paper ID: IJSRDV3I40803 Published in: Volume : 3, Issue : 4 Publication Date: 01/07/2015 Page(s): 1386-1389 |
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