engleski

Non-Parametric Context-Based Object Classification in Images

Segmentation and classification of objects in images is one of the most important and yet one of the most complex problems in computer vision. In this work we propose a new model for natural image object classification using contextual information at the level of image segments. Context modeling is largely independent of appearance-based classification and proposed model enables simple upgrade of existing systems with information from global and/or local context. Context modeling is based on non-parametric use of appearance-based classification results which is a novel approach compared to previous systems that model context on a limited number of rules expressed with a fixed set of parameters. Model implementation resulted in a system that, in our simulations, showed stable improvement of the appearance-based object classification.

Object classification, image context, image segmentation, natural images

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