Region Of Interest Extraction

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Region Of Interest Extraction

  1. 1. REGION OF INTEREST EXTRACTION Guide Prof. Shylaja. S. S Varun Kamath B HOD, ISE Gopi Krishnan Nambiar
  2. 2. INTRODUCTION <ul><li>Content Based Image Retrieval(CBIR) </li></ul><ul><li>Object Based Image Retrieval (OBIR) </li></ul><ul><li>Perception Based Image Retrieval (PBIR) </li></ul><ul><li>Region Of Interest </li></ul><ul><li>Saliency Map </li></ul><ul><li>Visual Attention Map </li></ul><ul><li>Itti – Koch Model </li></ul><ul><li>Stentiford Model </li></ul>
  3. 3. CONTENT BASED IMAGE RETRIEVAL (CBIR) <ul><li>CBIR refers to retrieval of images according to the content. </li></ul><ul><li>The purpose is to retrieve all the images which are relevant to user query. </li></ul>
  4. 4. CBIR <ul><li>Despite the large number of CBIR prototypes developed over the past 15 years, very few prototypes have experienced success or become popular commercial products . </li></ul><ul><li>Most of the CBIR solutions is based on addressing the problem using a biological approach i.e. the way human perceives the image. </li></ul><ul><li>The proposed models are applicable to image retrieval scenarios where one or few Regions of interest are present in each image. </li></ul>
  5. 5. OBJECT BASED IMAGE RETRIEVAL (OBIR) AND PERCEPTION BASED IMAGE RETRIEVAL (PBIR) <ul><li>OBIR refers to retrieval of regions or objects of interest within an image but not the image as a whole. </li></ul><ul><li>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. </li></ul>
  6. 6. REGION OF INTEREST (ROI) <ul><li>The region of interest is that part of the image which catches our attention instantly than the other parts of the image. </li></ul><ul><li>In the examples shown below, </li></ul><ul><li>Region of interest </li></ul>
  7. 7. SALIENCY MAP (S) <ul><li>It is a map which contains the most salient points of the image. </li></ul><ul><li>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. </li></ul><ul><li> Salient points </li></ul>
  8. 8. VISUAL ATTENTION MAP (VA) <ul><li>This map tends to identify larger and smoother salient regions of an image as opposed to identifying the most salient points in Saliency map. </li></ul><ul><li>This map is very much dependent on the salient regions of the image. </li></ul><ul><ul><ul><ul><ul><li>Areas of Attention </li></ul></ul></ul></ul></ul>
  9. 9. ITTI – KOCH MODEL (I-K MODEL) <ul><li>This model is used to identify the most salient points in an image. </li></ul><ul><li>It works with three low level dimension of images </li></ul><ul><ul><li>Colour. </li></ul></ul><ul><ul><li>Orientation. </li></ul></ul><ul><ul><li>Intensity. </li></ul></ul><ul><li>The I-K model outputs a list of image coordinates, each one corresponding to a point of attention (POA) </li></ul>
  10. 10. GENERAL ALGORITHM
  11. 11. SAMPLE OUTPUTS FROM ITTI – KOCH MODEL Original Image Saliency Map
  12. 12. STENTIFORD MODEL <ul><li>This captures the image regions which have distinctive and uncommon features. </li></ul><ul><li>It suppresses the areas of the image with repetitive colour patterns and enhances the salient ones. </li></ul><ul><li>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. </li></ul>
  13. 13. STENTIFORD MODEL Matching neighborhoods x and y
  14. 14. SAMPLE OUTPUTS FROM STENTIFORD MODEL Original Image Visual Attention Map
  15. 15. COMPARISON BETWEEN ITTI-KOCH AND STENTIFORD MODEL OUTPUTS Original Image Saliency Map VA Map
  16. 16. 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
  17. 17. GAUSSIAN PYRAMID <ul><li>A powerful and conceptually simple structure for representing images at more than one resolution </li></ul>
  18. 18. 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
  19. 19. CONCLUSIONS <ul><li>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. </li></ul><ul><li>This procedure can be used for effective Thumbnail Cropping and indexing. </li></ul>
  20. 20. DRAWBACKS <ul><li>Both the models are still incomplete (still under development) and hence not completely accurate. </li></ul><ul><li>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. </li></ul><ul><li>If the images are of poor quality and still ROIs are recognized by the human eye, the proposed models may not recognize them. </li></ul>
  21. 21. REFERENCES <ul><li>Extraction of Salient Regions of Interest Using Visual Attention Models </li></ul><ul><ul><li>Gustavo B. Borba and Humberto R. Gamba, Oge Marques and Liam M. Mayron </li></ul></ul><ul><li>An Attention-Driven Model for Grouping Similar Images with Image Retrieval Applications </li></ul><ul><ul><li>Oge Marques, Liam M. Mayron, Gustavo B. Borba and Humberto R. Gamba </li></ul></ul><ul><li>A Model of Saliency-Based Visual Attention for Rapid Scene Analysis </li></ul><ul><ul><li>Laurent Itti, Christof Koch, and Ernst Niebur </li></ul></ul><ul><li>An attention based similarity measure with application to content based information retrieval </li></ul><ul><ul><li>Fred W M Stentiford </li></ul></ul>

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