Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning
| Autor: | Qi-Jiunn Wu, Shang-Hong Lai, Szu-Hao Huang |
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| Rok vydání: | 2006 |
| Předmět: |
Computer Science::Machine Learning
Computer Networks and Communications business.industry Computer science Feature extraction Relevance feedback Pattern recognition Machine learning computer.software_genre k-nearest neighbors algorithm Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Discriminative model Hardware and Architecture Media Technology Artificial intelligence AdaBoost business computer Feature learning Image retrieval Software Information Systems |
| Zdroj: | Multimedia Systems. 12:14-26 |
| ISSN: | 1432-1882 0942-4962 |
| DOI: | 10.1007/s00530-006-0028-y |
| Popis: | Boost learning algorithm, such as AdaBoost, has been widely used in a variety of applications in multimedia and computer vision. Relevance feedback-based image retrieval has been formulated as a classification problem with a small number of training samples. Several machine learning techniques have been applied to this problem recently. In this paper, we propose a novel paired feature AdaBoost learning system for relevance feedback-based image retrieval. To facilitate density estimation in our feature learning method, we propose an ID3-like balance tree quantization method to preserve most discriminative information. By using paired feature combination, we map all training samples obtained in the relevance feedback process onto paired feature spaces and employ the AdaBoost algorithm to select a few feature pairs with best discrimination capabilities in the corresponding paired feature spaces. In the AdaBoost algorithm, we employ Bayesian classification to replace the traditional binary weak classifiers to enhance their classification power, thus producing a stronger classifier. Experimental results on content-based image retrieval (CBIR) show superior performance of the proposed system compared to some previous methods. |
| Databáze: | OpenAIRE |
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