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Region Of Interest Extraction
 

Region Of Interest Extraction

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    Region Of Interest Extraction Region Of Interest Extraction Presentation Transcript

    • REGION OF INTEREST EXTRACTION Guide Prof. Shylaja. S. S Varun Kamath B HOD, ISE Gopi Krishnan Nambiar
    • INTRODUCTION
      • Content Based Image Retrieval(CBIR)
      • Object Based Image Retrieval (OBIR)
      • Perception Based Image Retrieval (PBIR)
      • Region Of Interest
      • Saliency Map
      • Visual Attention Map
      • Itti – Koch Model
      • Stentiford Model
    • CONTENT BASED IMAGE RETRIEVAL (CBIR)
      • CBIR refers to retrieval of images according to the content.
      • The purpose is to retrieve all the images which are relevant to user query.
    • CBIR
      • Despite the large number of CBIR prototypes developed over the past 15 years, very few prototypes have experienced success or become popular commercial products .
      • Most of the CBIR solutions is based on addressing the problem using a biological approach i.e. the way human perceives the image.
      • The proposed models are applicable to image retrieval scenarios where one or few Regions of interest are present in each image.
    • OBJECT BASED IMAGE RETRIEVAL (OBIR) AND PERCEPTION BASED IMAGE RETRIEVAL (PBIR)
      • OBIR refers to retrieval of regions or objects of interest within an image but not the image as a whole.
      • PBIR is one of the most successful CBIR solutions which addresses the problem from a perceptual perspective and doing so using psychophysical approach i.e. towards stimulus and sensation of the image on the human eye.
    • REGION OF INTEREST (ROI)
      • The region of interest is that part of the image which catches our attention instantly than the other parts of the image.
      • In the examples shown below,
      • Region of interest
    • SALIENCY MAP (S)
      • It is a map which contains the most salient points of the image.
      • For example if one wants to find a red object in an image, then saliency map will be biased to consider red more than other features.
      • Salient points
    • VISUAL ATTENTION MAP (VA)
      • This map tends to identify larger and smoother salient regions of an image as opposed to identifying the most salient points in Saliency map.
      • This map is very much dependent on the salient regions of the image.
              • Areas of Attention
    • ITTI – KOCH MODEL (I-K MODEL)
      • This model is used to identify the most salient points in an image.
      • It works with three low level dimension of images
        • Colour.
        • Orientation.
        • Intensity.
      • The I-K model outputs a list of image coordinates, each one corresponding to a point of attention (POA)
    • GENERAL ALGORITHM
    • SAMPLE OUTPUTS FROM ITTI – KOCH MODEL Original Image Saliency Map
    • STENTIFORD MODEL
      • This captures the image regions which have distinctive and uncommon features.
      • It suppresses the areas of the image with repetitive colour patterns and enhances the salient ones.
      • This is done by measuring colour dissimilarities between random neighbourhoods in the image and assigning high scores to the most dissimilar pixels in the entire image.
    • STENTIFORD MODEL Matching neighborhoods x and y
    • SAMPLE OUTPUTS FROM STENTIFORD MODEL Original Image Visual Attention Map
    • COMPARISON BETWEEN ITTI-KOCH AND STENTIFORD MODEL OUTPUTS Original Image Saliency Map VA Map
    • A general view of the proposed VA-based ROI extraction method. Proposed ROI Extraction Index POA - Point of Attention AOA - Area of Attention VA – Visual Attention
    • GAUSSIAN PYRAMID
      • A powerful and conceptually simple structure for representing images at more than one resolution
    • EXAMPLE OF GAUSSIAN PYRAMIDING Figure 6: The Gaussian pyramid. The original image is repeatedly filtered and sub sampled to generate the sequence of reduced resolution images
    • CONCLUSIONS
      • Since the models produce their own ROIs, which may or may not match with each others’ maps (here referring to Itti-Koch and Stentiford models), better output can be derived i.e. by combining both the common ROIs of the respective maps.
      • This procedure can be used for effective Thumbnail Cropping and indexing.
    • DRAWBACKS
      • Both the models are still incomplete (still under development) and hence not completely accurate.
      • If there are many ROIs in an image, all of them may still not be recognized because the models are not completely perfect in recognizing every ROI.
      • If the images are of poor quality and still ROIs are recognized by the human eye, the proposed models may not recognize them.
    • REFERENCES
      • Extraction of Salient Regions of Interest Using Visual Attention Models
        • Gustavo B. Borba and Humberto R. Gamba, Oge Marques and Liam M. Mayron
      • An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications
        • Oge Marques, Liam M. Mayron, Gustavo B. Borba and Humberto R. Gamba
      • A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
        • Laurent Itti, Christof Koch, and Ernst Niebur
      • An attention based similarity measure with application to content based information retrieval
        • Fred W M Stentiford