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Automatic object segmentation using perceptual grouping of regions with contextual constraints


Citation

Zand, Mohsen and C. Doraisamy, Shyamala and Abdul Halin, Alfian and Mustaffa, Mas Rina (2015) Automatic object segmentation using perceptual grouping of regions with contextual constraints. In: 5th International Conference on Image Processing, Theory, Tools and Applications 2015 (IPTA 2015), 10-13 Nov. 2015, Orleans, France. (pp. 530-534).

Abstract

Image segmentation is still considered a very challenging subject despite years of research effort poured into the field. The problem is exacerbated when there is need for specific object detection. Since objects can be visually non-homogeneous, techniques that attempt to segment images into visually uniform regions using only the bottom-up cues, tend to fail. We propose a novel two-step model that incorporates both bottom-up information and top-down object constraints. Firstly, a set of uniform regions are generated using an extension of contour detection, seeded region growing, and graph-based methods. The second step applies co-occurrence constraints on the image regions in order to perceptually group regions into objects. This unsupervised segmentation process models each object using higher-level knowledge in the form of visual co-occurrences of its constituent parts. Experiments on the horse and ImageCLEF databases show that the proposed technique performs comparably well with existing state-of-the-art techniques.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1109/IPTA.2015.7367203
Publisher: IEEE
Keywords: Automatic image segmentation; Contextual relationships; Graph-based segmentation; Object segmentation
Depositing User: Nabilah Mustapa
Date Deposited: 31 Jul 2017 05:22
Last Modified: 31 Jul 2017 05:22
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/IPTA.2015.7367203
URI: http://psasir.upm.edu.my/id/eprint/56313
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