Objective-guided Image Annotation

Publisher:
IEEE-Inst Electrical Electronics Engineers Inc
Publication Type:
Journal Article
Citation:
IEEE Transactions On Image Processing, 2013, 22 (4), pp. 1585 - 1597
Issue Date:
2013-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013004112OK.pdf1.18 MB
Adobe PDF
Automatic image annotation, which is usually formulated as a multi-label classification problem, is one of the major tools used to enhance the semantic understanding of web images. Many multimedia applications (e.g., tag-based image retrieval) can greatly benefit from image annotation. However, the insufficient performance of image annotation methods prevents these applications from being practical. On the other hand, specific measures are usually designed to evaluate how well one annotation method performs for a specific objective or application, but most image annotation methods do not consider optimization of these measures, so that they are inevitably trapped into suboptimal performance of these objective-specific measures. To address this issue, we first summarize a variety of objective-guided performance measures under a unified representation. Our analysis reveals that macro-averaging measures are very sensitive to infrequent keywords, and hamming measure is easily affected by skewed distributions. We then propose a unified multi-label learning framework, which directly optimizes a variety of objective-specific measures of multi-label learning tasks. Specifically, we first present a multilayer hierarchical structure of learning hypotheses for multi-label problems based on which a variety of loss functions with respect to objective-guided measures are defined
Please use this identifier to cite or link to this item: