Paper Info
Title
Image retrieval algorithm based on enhanced relational graph
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.
Year
DOI
Venue
2011
10.1007/978-3-642-21822-4_23
IEA/AIE (1)
Keywords
Field
DocType
semi-supervised learning,semi-supervised learning algorithm,image retrieval algorithm,entire data,enhanced relational matrix,semi-supervised learning framework,enhanced relational graph,effective methodology,novel algorithm,proposed algorithm,unlabeled data,potential intra-class data,graph embedding,image retrieval,semi supervised learning,semantic gap,manifold learning
Semi-supervised learning,Relevance feedback,Computer science,Matrix (mathematics),Image retrieval,Unsupervised learning,Artificial intelligence,Nonlinear dimensionality reduction,Pattern recognition,Graph embedding,Semantic gap,Algorithm,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
4
Name
Order
Citations
PageRank
Guang-Nan He151.41
Yubin Yang227733.46
Ning Li3105.57
Yao Zhang4214.52