Content Analysis using Shape and Spatial Layout with Markov Random Field

Background/Objectives: Unstructured random scene perception and understanding is a challenging problem in the field of computer vision and image processing. Methods/Statistical analysis: Two images with very different appearance may have the same color, texture and shape features. To recognize two different pictures having similar color, texture and shape we can apply spatial investigation. Findings: A methodology in the light of Markov random field has been proposed in this work to recuperate the fundamental spatial layout from a solitary image and start to examine its use as a foundation for scene understanding and content analysis in content based image retrieval. Our representation comprises of three key components (1) coarsely depicting the orientation of significant scene surfaces, (2) an examination of low level features for the understanding of image, and (3) a shape obliged Markov random field definition that enforces shape priors over the regions. Application/Improvements: We experimentally assess different Markov random field formations and exhibit the adequacy of our proposed approach in scene understanding and content analysis.