報告人：張景舜 P.H. Wu, C.C. Chen, J.J. Ding, C.Y. Hsu, and Y.W. Huang IEEE Transactions on Image Processing, Vol. 22, No. 9, September 2013 Salient Region Detection Improved by Principle Component Analysis and Boundary Information 1

Outline  Introduction.  Background  Framework  Approach  Simulations  Conclusions 2

INTRODUCTION Many of the existing saliency detection methods do not pay much attention to the noise problem. The framework in this paper adopts both the L0 smoothing filter and PCA to reduce the effect of noise and achieve a better performance. the process takes an average of 1.13 seconds per image computed on a 2.8 GHz Intel Core i5 CPU using the released MATLAB code. 3

INTRODUCTION 4 Saliency maps  color spatial variance  border measurement  local-global contrast Top: Original images. Middle: Saliency maps generated from the proposed method. Bottom : Human labeled ground truths

BACKGROUND 一、 L0 Smoothing Filter ( 一 ) A demonstration of the L0 smoothing filter: (a) the original image (b) the smoothed image after removing small-magnitude gradients. 5

BACKGROUND ( 二 ) An illustration of the L0 smoothing filter. (a) The intensity of a row from the original image. (b) The result after applying the L0 smoothing filter. Note that only the prominent intensity changes are preserved. 6

7 二、 Principle Component Analysis(PCA) 1. Extract the main colors of images. 2. Feature extraction and dimensionality reduction. 3. PCA functionalities (1) noise reduction (2) translation error (3) Attenuation (4)ellipse fitting (5) solving for non-full-rank eigenproblems.

名詞解釋 8 PCA ： Principle Component Analysis KL transform ： Karhunen-Loeve transform GMM ： Gaussian Mixture Model CSV ： color spatial variance BM ： border measurement BS ： boundary scoring CA ： Context Aware Dis ： the central distance map database ： ASD ， SED ， SOD ， BSD

FRAMEWORK 9

IV. APPROACH 10 一、 Color Spatial Variance(CSV) Color spatial variance based on the GMM is a widely used global feature that matches the human visual system. If a color is extensively distributed within an image, it may be the background color. In other words, a specific color with a smaller spatial variance will attract greater attention, and is more likely to be part of the salient object.

IV. APPROACH 11 二. Image Segmentation for Border Measurement using the L0 smoothing fi lter before segmentation can prevent over-segmentation. salient objects rarely connect with image borders, an adaptive region- merging method and border measurement

IV. APPROACH 12 三、 PCA Context-Aware 1. Dimensionality reduction for decreasing the computational complexity; 2. Noise reduction; 3. Alleviation of translation errors.

IV. APPROACH 13 D. Boundary Scoring The salient regions are highlighted more than the background, thus alleviating the effect of an incomplete segment result. boundary of each segment to determine the saliency value rather than the entire segment, since the CA method is more effective around edges. he utilization of the boundary can lower the influence of an incomplete segmentation result.

IV. APPROACH 14

V. SIMULATIONS 15 一、 Performance Measurement 1. Precision-recall rate 2. F-measure W and H are the width and height of the saliency map S, respectively. Ta is twice the mean saliency of the image. β is set to 0.3

V. SIMULATIONS 16 三、 Application

VI. CONCLUSIONS 17 Reduce noise and other redundant information Increase both the accuracy and efficiency of saliency detection The color special variance, border information, and global-local contrast were utilized to construct the saliency maps. Our proposed method achieved higher precision-recall rates and F-measure than other state-of-the-art methods

CONCLUSION L0 smoothing filter and PCA canreduce noise and other redundant information and increase both the accuracy and efficiency of saliency detection. the color special variance, border information, and global-local contrast were utilized to construct the saliency maps. ASD, SED,and SOD and the simulation results showed this proposed method achieved higher precision-recall rates and F-measurethan other state-of-the-art methods. 18

Thanks for listening 19