Non‐concept density estimation via kernel regression for concept ranking in weakly labelled data

Abstract

Automatic object annotation for weakly labelled images/videos has attracted great research interests. In the literature, the idea of negative mining has been proposed for the task. Following existing works, the authors start with image/video over‐segmentation. With the assumption that the noisy segments in the concept images and the strongly labelled non‐concept segments are drawn from the same distribution, the authors plan to estimate the non‐concept distribution and apply it to the ambiguous segments to generate a concept ranking. Although this idea was proposed in existing work and was shown ineffective when combined with a naive kernel density estimation strategy, in this study, the authors explore improved density estimation techniques for the ranking and propose a kernel regression model whose parameters are estimated by a maximum likelihood estimation. Experimental results validate the effectiveness of their method.

Keywords

• nonconcept density estimation
• kernel regression model
• concept ranking
• automatic object annotation
• weakly labelled images
• weakly labelled videos