Name
Abstract | ||
|---|---|---|
The "semantic gap" problem is one of the main difficulties in image retrieval tasks. Semi-supervised learning, typically integrated with the relevance feedback techniques, is an effective method to narrow down the semantic gap. 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 its effectiveness of using the relationships between the labeled and unlabeled data. This paper proposes a novel algorithm to better explore those relationships by augmenting the relational graph representation built on the entire data set, expected to increase the intra-class weights while decreasing the inter-class weights and linking the potential intra-class data. The augmented relational matrix can be directly used in any semi-supervised learning algorithms. The experimental results in a range of feedback-based image retrieval tasks show that the proposed algorithm not only achieves good generality, but also outperforms other algorithms in the same semi-supervised learning framework. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10489-012-0370-z | Appl. Intell. |
Keywords | Field | DocType |
Graph embedding,Image retrieval,Manifold learning,Relevance feedback | Semi-supervised learning,Relevance feedback,Pattern recognition,Effective method,Computer science,Graph embedding,Semantic gap,Image retrieval,Artificial intelligence,Nonlinear dimensionality reduction,Machine learning,Graph (abstract data type) | Journal |
Volume | Issue | ISSN |
38 | 4 | 0924-669X |
Citations | PageRank | References |
3 | 0.36 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yubin Yang | 1 | 277 | 33.46 |
| Ya-Nan Li | 2 | 3 | 0.36 |
| Ling-Yan Pan | 3 | 8 | 1.77 |
| Ning Li | 4 | 10 | 5.57 |
| Guang-Nan He | 5 | 5 | 1.41 |