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

摘要

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. On the basis of this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multi-relational association rule mining where the relations exist between image-based features, and textual 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.