1.  Mentor : Prof. Amitabha Mukerjee Learning to Detect Salient Objects Team Members - Avinash Koyya Diwakar Chauhan

2.   Salient Object : Object of visual attention  Foreground object  Familiar but unknown  Aim : To detect such object What is it?

3.   Fitting image to smaller screen  Image Collection browsing  Tracking objects Why is it?

4.   Itti’s algorithm  Colors, Intensity, orientation  Require parameter setting for good result  Matlab saliency toolbox  “Learning to Detect Salient object” by Tie Liu, 2011 Previous work

5.   Binary classification of each pixel  Bottom up model Approach Salient Object Features Pairwise Feature Conditional Distribution Linear Combination CRF Learning Salient Background

6.   Multiscale Contrast  Center -Surround Histogram  Color spatial Distribution Salient object features

7.   Sudden sharp change of intensity?  Simulation of human visual receptive field  Can carry the point of attention Multiscale Contrast ImageGaussian Filter Contrast map Rescale to ½ of previous Filtered Image Multiscale Contrast Map and sumRescale

8.  Examples Input Output Intermediate Contrast outputs in Pyramid

9.   Regional feature.  Attention proportional to distinction from the surroundings. Center Surround Histogram Integral Histogram Chi-Square distance Feature map Aspect ratios ; Sizes Maximize Weighted Sum

10. Center Surround histogram distances with different locations and sizes.

11. Original Image Output claimed by the paper Output we obtained

12.   Global feature.  Wider a colour is distributed in the image, the less possible it is that a salient object contains this colour. Colour Spatial Distribution K-means Gaussian Mixture Models Spatial Variance EM algorithm Feature MapCenter Weight Weighted Sum

13. Original Image Output claimed by the paper Output we obtained

14.   We are using MSRA salient object database  20,000 training images and 5,000 test data  Training images are labeled by three people Database

15.   Process of learning from the images  Learn the linear weights in under maximum likelihood criteria CRF Learning

16.   Feature maps ready  Labeling of database and CRF learning in progress So far…