Feature-Independent Context Estimation For Automatic Image Annotation

Abstract

Automatic image annotation is a highly valuable tool for image search, retrieval and archival systems. In the absence of an annotation tool, such systems have to rely on either users' input or large amount of text on the webpage of the image, to acquire its textual description. Users may provide insufficient/noisy tags and all the text on the webpage may not be a description or an explanation of the accompanying image. Therefore, it is of extreme importance to develop efficient tools for automatic annotation of images with correct and sufficient tags. The context of the image plays a significant role in this process, along with the content of the image. A suitable quantification of the context of the image may reduce the semantic gap between visual features and appropriate textual description of the image. In this paper, we present an unsupervised feature-independent quantification of the context of the image through tensor decomposition. We incorporate the estimated context as prior knowledge in the process of automatic image annotation. Evaluation of the predicted annotations provides evidence of the effectiveness of our feature-independent context estimation method.

Publication Date

10-14-2015

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volume

07-12-June-2015

Number of Pages

1958-1965

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2015.7298806

Socpus ID

84959201191 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84959201191

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