Visual Saliency: Learning to Detect Salient Objects

Visual Attention: Detecting Saliency on Images

Vicente OrdonezDepartment of Computer Science

State University of New YorkStony Brook, NY 11790

I will be working mainly on the following paper

• Learning to Detect a Salient Object. T. Liu, J. Sun, N. Zheng, X. Tang, H. Shum. (Xian Jiaotong University and Microsoft Research Asia) from CVPR 2007.

http://research.microsoft.com/en-us/um/people/jiansun/papers/SalientDetection_CVPR07.pdf




What is Saliency? What is Visual Attention?

“Everyone knows what attention is...”—William James, 1890

This is a problem of…

• Arbitrary object detection?• Background / Foreground segmentation?• Modeling Visual Attention?

The Method

• Features: – Multiscale Contrast (Done!)– Center surround histogram (Mostly Done!) (Done!)– Color spatial distribution (Done!)

• Supervised learning using Conditional Random Fields to determine the parameters to combine the features obtained above. (Done!) [I will use a labeled dataset of 5000 images provided by Microsoft Research Asia!]

Multiscale Contrast Function

• Generate the Gaussian Pyramid for the input image.– For each level in the pyramid • Do gaussian blurring• Do resampling

I’m using a 6 levels Gaussian pyramid for each RGB channel.

How a Gaussian pyramid looks like

Figure from David Forsyth

• Generate contrast maps for each level of the Pyramid.

• Sum all of the results to produce the final multiscale contrast map.– The two steps mentioned above are described in

this formula:

Multiscale Contrast Function

Input image

Contrast maps

Contrast maps

Original image Contrast map at level 1

Contrast map at level 4 Contrast map at level 6

Multiscale Contrast Map Output

Center Surround Histogram Feature

For each pixel in the image For each possible rectangle with a reasonable size and

aspect ratio Create a surrounding rectangle and calculate the

histogram of the rectangle and the surrounding area. Pick and record the rectangle that maximizes the Chi-

Square distance between the two histograms calculated above and also record the Chi-Square distance.


• The algorithm as described before is computationally expensive…

• It is required to use a technique called Integral Histogram. It allows you fast calculation of the histogram of any given rectangular region of an image.

• The algorithm was introduced in:– “Integral Histogram: A Fast Way to Extract Histograms

in Cartesian Spaces” by Fatih Porikli, Mitsubishi Electric Research Lab in CVPR 2005.


• Use the Chi Square Distances Map and the Map of Most Salient Rectangle Regions per pixel to generate the Center Surround Histogram Feature using the next formula:

Center Surround Histogram

Results Using my Implementation (15.2 sec, size = 245x384)

Results Reported in the Paper


Results Reported in the Paper


More Results

More results

More Results

Color Spatial Distribution


• Make an initial clustering of the colors in the image using k-means.

• Further refine the clusters by using Gaussian Mixture Models. The Gaussian Mixture Model parameters are calculated using the EM algorithm.

• I am using 5 clusters (5 colors) per image. And the results look similar to those presented in the paper with an execution time of around 17 seconds per image.


• Calculate the vertical variance of the horizontal positions of the pixels for each cluster. And then the same for the vertical positions. Sum the variances and use this value to weight more those clusters with less spatial variance.

• Penalize the clusters that contain the majority of its pixels away from the center of the image.

Combine Features Together

Conditional Random Field Training and Inference

• Accelerated Training of Conditional Random Fields with Stochastic Meta-Descent S Vishwanathan, N. Schraudolph, M. Schmidt, K. Murphy. ICML'06 (Intl Conf on Machine Learning).

• I did the training using this toolbox from the above paper:

• http://people.cs.ubc.ca/~murphyk/Software/CRF/crf.html

http://people.cs.ubc.ca/~murphyk/Papers/icml06_camera.pdf

http://people.cs.ubc.ca/~murphyk/Papers/icml06_camera.pdf

http://people.cs.ubc.ca/~murphyk/Software/CRF/crf.html

http://people.cs.ubc.ca/~murphyk/Software/CRF/crf.html

Mask outputs using CRF inferenceInput M-Contrast-map Center Surr. Hist. Color Spatial Var.

Input Combined features Ground truth

Precision / Recall obtained

Multiscale Contrast


Color Spatial Variance

Combination (Training Dataset =

100)

Combination (Larger

Dataset = 2000)

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PrecisionRecallF-measure

Some Conclusions

• The results of the original research paper on computing the visual features have been successfully replicated in a considerable extent.

• The Conditional Random Field framework used in this project turned out to perform well for this task.

• The center-surround histogram map turned out to be the feature that gave the higher precision.

• The amount of time required for computing the individual features is in the order of several seconds.

Visual Saliency: Learning to Detect Salient Objects

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