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2006

CoMMA: a framework for integrated multimedia mining using multi-relational associations

8 years 6 months ago
CoMMA: a framework for integrated multimedia mining using multi-relational associations
Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. Based on this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multirelational association rule mining where the relations exist between image-based features, and texual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images. In this paper we also present a multi-relational extension to the FP-Tree algorithm to accomplish the association rule mining task more effectively compared to the currently us...
Ankur Teredesai, Muhammad A. Ahmad, Juveria Kanodi
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where KAIS
Authors Ankur Teredesai, Muhammad A. Ahmad, Juveria Kanodia, Roger S. Gaborski
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