Research Article Open Access

Object Recognition Based on Image Segmentation and Clustering

S. Thilagamani and N. Shanthi

Abstract

Problem statement: This study deals with object recognition based on image segmentation and clustering. Acquiring prior information of an image is done via two separate processes. Approach: The first process deals with detecting object parts of an image and integration of detected parts into several clusters. All these cluster centers form the visual words. The second process deals with over segmenting the image into super pixels and formation of larger sub region using Mid-level clustering algorithm, since it incorporates various information to decide the homogeneity of a sub region. Results: The outcome of the two processes are used for the similarity graph representation for object segmentation as proposed. In order to model the relationship between the shape and color or texture matrix representation has been used. Mask map ensures that the probability of each super pixel to harp inside an object. Conclusion: The basic whim is to integrate all the priors into an uniform framework. Thus the ORBISC can handle size, color, texture and pose variations better than those methods that focus on the objects only.

Journal of Computer Science
Volume 7 No. 11, 2011, 1741-1748

DOI: https://doi.org/10.3844/jcssp.2011.1741.1748

Submitted On: 8 June 2011 Published On: 6 September 2011

How to Cite: Thilagamani, S. & Shanthi, N. (2011). Object Recognition Based on Image Segmentation and Clustering. Journal of Computer Science, 7(11), 1741-1748. https://doi.org/10.3844/jcssp.2011.1741.1748

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Keywords

  • Super pixel
  • oversegmentation
  • similarity dependence graph
  • euclidean distance measure
  • object recognition
  • super pixels
  • object segmentation
  • image editing
  • graph representation
  • euclidean distance formula