Multiple Feature Relevance Feedback in Content- Based Image Retrieval using Probabilistic Inference Networks

2005 ◽  
pp. 197-208
Author(s):  
Campbell Wilson ◽  
Bala Srinivasan
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Probabilistic Inference ◽  
Content Based Image Retrieval ◽  
Multiple Feature ◽  
Feature Relevance

scholarly journals Content Based Image Retrieval Using SVM Algorithm

2013 ◽  
pp. 207-210
Author(s):  
K. Ashok Kumar ◽  
Y.V. Bhaskar Reddy
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Data Cleaning ◽  
Image Similarity ◽  
Content Based Image Retrieval ◽  
Practical Applications ◽  
Multiple Feature ◽  
Svm Algorithm ◽  
Noise Tolerant ◽  
Noisy Examples

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.

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