• DocumentCode
    1776376
  • Title

    Weighted pixel aggregation segmentation on outdoor scene images

  • Author

    Dileep, Deepika ; Nair, Rashmi S.

  • Author_Institution
    CSE Dept., Mohandas Coll. of Eng. & Technol., Nedumangad, India
  • fYear
    2014
  • fDate
    10-11 July 2014
  • Firstpage
    819
  • Lastpage
    823
  • Abstract
    In image analysis, segmentation is the process of partitioning a digital image into homogeneous and self consistent region. A different approach that has recently gained popularity is to apply graph algorithms to segmentation. This paper proposes segmentation by weighted approach and hence compares with segmentation on an outdoor scene image based on background recognition and perceptual organization. This model is developed to capture structural relationship among the constituent parts of the structured objects. Hence it groups the objects together without any prior knowledge of the specific objects. This segmentation algorithm is purely inspired by Perceptual Organization Method and further this method incorporates a weighted aggregation approach integrated into the graph coarsening scheme and compares and evaluates the two approaches.
  • Keywords
    image recognition; image segmentation; natural scenes; pattern clustering; background recognition; digital image partitioning; graph coarsening scheme; image analysis; k-means clustering; outdoor scene images; perceptual organization method; weighted pixel aggregation segmentation; Aggregates; Algorithm design and analysis; Histograms; Image color analysis; Image segmentation; Object recognition; Organizations; Bottom up aggregation; Cues; Image segmentation; K-means Clustering; Perceptual organization; Weighted Segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on
  • Conference_Location
    Kanyakumari
  • Print_ISBN
    978-1-4799-4191-9
  • Type

    conf

  • DOI
    10.1109/ICCICCT.2014.6993071
  • Filename
    6993071