Modelling of spatial causality among distinctive properties of an image using conditional random field for image classification

ABSTRACT

In this paper, we proposed an ordered patch-based method using conditional random field (CRF) in order to encode local properties and their spatial relationship in the images to address texture classification, face recognition and scene classification problems. Typical image classification approaches classify images without considering spatial causality among distinctive properties of an image to represent it in the feature space. In this method first, each image is encoded as a sequence of ordered patches including local properties. Second, the sequence of these ordered patches is modelled as a probabilistic feature vector using CRF to model spatial relationship of these local properties; and finally, image classification is performed on such probabilistic image representation. Experimental results on several standard image datasets indicate that the proposed method outperforms some of existing image classification methods.

KEYWORDS:

• Conditional random field
• gist
• image classification
• spatial information
• SVM

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

The images used in Figures 1 and 5 have been obtained from online archival databases, and are available at the following links:

The ORL Database of Faces

http://www.cl.cam.ac.uk/research/dtg/attarchive/

The Yale Face Database http://web.mit.edu/emeyers/www/face_databases.html

The Yale Face Database B

http://web.mit.edu/emeyers/www/face_databases.html#yaleB