Name
Abstract | ||
|---|---|---|
Novel large margin formulation for semantic similarity learning.Efficient optimization algorithm to solve the proposed semi-definite program (SDP).Thorough experimental study to compare the performances of several algorithms for hierarchical image classification.State-of-the-art classification performance under the hierarchical-loss criterion. In the present paper, a novel image classification method that uses the hierarchical structure of categories to produce more semantic prediction is presented. This implies that our algorithm may not yield a correct prediction, but the result is likely to be semantically close to the right category. Therefore, the proposed method is able to provide a more informative classification result. The main idea of our method is twofold. First, it uses semantic representation, instead of low-level image features, enabling the construction of high-level constraints that exploit the relationship among semantic concepts in the category hierarchy. Second, from such constraints, an optimization problem is formulated to learn a semantic similarity function in a large-margin framework. This similarity function is then used to classify test images. Experimental results demonstrate that our method provides effective classification results for various real-image datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.cviu.2014.11.006 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
image classification | Similarity learning,Semantic similarity,Pattern recognition,Feature (computer vision),Exploit,Artificial intelligence,Hierarchy,Contextual image classification,Optimization problem,Semantic computing,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
132 | C | 1077-3142 |
Citations | PageRank | References |
4 | 0.39 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ju Yong Chang | 1 | 105 | 9.64 |
| Kyoung Mu Lee | 2 | 3228 | 153.84 |