Contextual object categorization with energy-based model

TL;DR

This paper introduces an energy-based model that fully utilizes spatial and contextual information, including fuzzy spatial relations, for improved object categorization in images, addressing limitations of previous probabilistic approaches.

Contribution

The work proposes a novel energy-based model incorporating fuzzy spatial relations and addresses the intractable partition function challenge in object categorization.

Findings

Effective utilization of fuzzy spatial relations improves categorization accuracy.

The model successfully addresses the intractable partition function problem.

Experimental results confirm the method's validity and reliability.

Abstract

Object categorization is a hot issue of an image mining. Contextual information between objects is one of the important semantic knowledge of an image. However, the previous researches for an object categorization have not made full use of the contextual information, especially the spatial relations between objects. In addition, the object categorization methods, which generally use the probabilistic graphical models to implement the incorporation of contextual information with appearance of objects, are almost inevitable to evaluate the intractable partition function for normalization. In this work, we introduced fully-connected fuzzy spatial relations including directional, distance and topological relations between object regions, so the spatial relational information could be fully utilized. Then, the spatial relations were considered as well as co-occurrence and appearance of objects by using energy-based model, where the energy function was defined as the region-object association potential and the configuration potential of objects. Minimizing the energy function of whole image arrangement, we obtained the optimal label set about the image regions and addressed the evaluation of intractable partition function in conditional random fields. Experimental results show the validity and reliability of this proposed method.