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...

• Type: Journal
• Year: 2006
• Where: KAIS
• Authors: Ankur Teredesai, Muhammad A. Ahmad, Juveria Kanodia, Roger S. Gaborski
• Keywords: Association Rule, Association Rule Mining, Image, KAIS 2006