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Content Based Image Retrieval Using Unclean Positive Examples
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AI
DOI：10.1109/TIP.2009.2026669.png)
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Abstract
En 中文
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.
Keywords:
Classifier combination
content-based image retrieval (CBIR)
feature aggregation
noise tolerant
support vector machine (SVM)
Journal
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13.7
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1.0W
Citations:
8.4W