Global optimization in discretized parameter space for predefined object segmentation

  • Authors:

  • Huy Hoang Nguyen;Hyukro Park;Joon Seub Cha;Le Thi Khue Van;Gueesang Lee

  • Affiliations:

  • Chonnam National University, Bukgu, Gwangju, Korea;Chonnam National University, Bukgu, Gwangju, Korea;Honam University, Seobong-dong Gwangsan-gu, Gwangju, Korea;Chonnam National University, Bukgu, Gwangju, Korea;Chonnam National University, Bukgu, Gwangju, Korea

  • Venue:
  • Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
  • Year:
  • 2013

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Abstract

In object segmentation field, while the non-predefined object segmentation distinguishes arbitrary self-assumed object from background, predefined object segmentation pre-specifies object evidently. This paper presents a new method to segment predefined objects by globally optimizing an orientation-based objective function that measures the fitness of object boundary in a discretized parameter space. A specific object is explicitly described by normalized discrete sets of boundary points and corresponding normal vectors with respect to its plane shapes in a certain aspect. The orientation factor provides robust distinctness for target objects. By considering the order relation of transformation elements, and their dependency on derived over-segmentation outcome, the domain of translations and scales is discretized efficiently. The appropriate transformation parameters of a shape model corresponding to a target object in an image are determined using the global optimization algorithm branch-bound. Discrete boundary points of the consequent transformed model are chained together to produce the final contour of the target object. The results tested on PASCAL dataset show a considerable achievement in solving complex background and unclear boundary images.