Name
Abstract | ||
|---|---|---|
Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM ((MLS)-S-2-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the (MLS)-S-2-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of (MLS)-S-2-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, (MLS)-S-2-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1108/EL-09-2019-0207 | ELECTRONIC LIBRARY |
Keywords | Field | DocType |
Support vector machine,Multi-label learning,LS-SVM,Image classification | Hamming code,F1 score,Data set,Pattern recognition,Subspace topology,Least squares support vector machine,Computer science,Support vector machine,Semantic information,Artificial intelligence,Contextual image classification,Multimedia | Journal |
Volume | Issue | ISSN |
37.0 | 6.0 | 0264-0473 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||