Abstract
The “semantic gap” problem is one of the main difficulties in image retrieval task. Semi-supervised learning is an effective methodology proposed to narrow down the gap, which is also often integrated with relevance feedback techniques. However, in semi-supervised learning, the amount of unlabeled data is usually much greater than that of labeled data. Therefore, the performance of a semi-supervised learning algorithm relies heavily on how effective it uses the relationship between the labeled and unlabeled data. A novel algorithm is proposed in this paper to enhance the relational graph built on the entire data set, expected to increase the intra-class weights of data while decreasing the inter-class weights and linking the potential intra-class data. The enhanced relational matrix can be directly used in any semi-supervised learning algorithm. The experimental results in feedback-based image retrieval tasks show that the proposed algorithm performs much better compared with other algorithms in the same semi-supervised learning framework.
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He, GN., Yang, YB., Li, N., Zhang, Y. (2011). Image Retrieval Algorithm Based on Enhanced Relational Graph. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_23
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DOI: https://doi.org/10.1007/978-3-642-21822-4_23
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