region-based saliency detection and its application in object recognition ieee transactions on...
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1
Region-Based Saliency Detection and Its Application in Object Recognition
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEM FOR VIDEO TECHNOLOGY, VOL. 24
NO. 5, MAY 2014
Zhixiang Ren, Shenghua Gao, liang-Tien Chia, and Ivor Wai-Hung Tsang
2
Overview
• Introduction
• Related Work
• Proposed Method of Saliency
• Experiments For Saliency Detection
• Conclusion
3
Introduction
4
Introduction
• Visual Saliency
Measure to what extent a region attracts human attention.
• Potential Application
Adaptive compression, image retargeting, object detection
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Introduction (cont.)
• Many saliency detection algorithms (pixel-grid) have been proposed
[36]-[38], [57], [68]
• Drawbacks
• Perform poorly in the images with large salient regions
• Suffer from the messy background, e.g. natural scenes.
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Introduction (cont.)
• [17], [18] suggest that early feature like color, contrast, and orientation indirectly affect human attention
Human is attracted by objects not by individual pixels
• It is natural to work with those perceptually meaningful image regions in saliency detection
Concept of superpixel [54]
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Introduction (cont.)
• Proposed work applies two existing techniques to improve saliency detection
Superpixel representation – Used to represent the input image
PageRank algorithm – Applied to propagate saliency among similar clusters and refine saliency map
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Related Work
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Related Work
• Saliency detection methods can be divided into two categories
Top-down method : Task-dependent and based on prior knowledge about the object and their interrelations
Bottom-up method : Hypothesis for saliency is that salient stimulus is distinct from its surrounding stimuli. (contrast)
• For bottom-up method, research usually focus on identifying those regions with high contrast.
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Related Work (cont.)
• [38] proposed to determine the contrast by DoG
• [57] measured the likeness of a pixel to its surroundings by the local regression kernels
• [1] measured the saliency of each pixel by the difference between the feature of each pixel and mean of the whole image.
• [68] measured the global contrast with all the other pixels.
• [30] model both local and global contrast by taking the positional distance into account
• Most of approach represent the input image in pixel-grid manner, and these method may failed to detect the homogeneous and quite large salient objects
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Related Work (cont.)
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Proposed Method
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Proposed Method
• Superpixel Extraction and Clustering
• Salient Region Detection
• Saliency Refinement With Propagation
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Superpixel Extraction
• Given an input image, mean shift algorithm[13] will be performed in color space to extract superpixels.
• In mean shift algorithm, , , and are needed.
Spatial Radius
Range Radius
Minimum Point Density
Maximum range radius
Average color variance
Color variance
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Superpixel Extraction (cont.)
• Mean shift
Mean shift is a procedure for locating the maxima of a density function given discrete data sampled from that function.
Scale parameter
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Superpixel Extraction (cont.)
m(x)
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Superpixel Clustering
• After mean shift, every superpixel will obtain a unique RGB color
• GMM is introduced to cluster superpixels in RGB color space
• The RGB value of this superpixel will be set as a 3-D vector to represent the superpixel during GMMR 139
G 160
B 127
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Superpixel Clustering (cont.)
• K-means is used to initialize the GMM
• Expectation maximization algorithm is used to train GMM parameter [5]
• The probability of the th superpixel belong to the th cluster
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Superpixel Clustering (cont.)
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Superpixel Clustering (cont.)
• Mixture density
Assume the set of N training samples is drawn from a mixture of models
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Superpixel Clustering (cont.)
• How to estimate and
EM algorithm
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Superpixel Clustering (cont.)
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Superpixel Clustering (cont.)
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Superpixel Clustering (cont.)
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Superpixel Clustering (cont.)
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Salient Region Detection
• Idea : background has larger spread in spatial domain
• i.e., the more compact the clusters are spread, the more salient they will be
• [32] proposed compactness metric to evaluate the spread of cluster
• Inter-cluster distances defined as
Cluster Spatial Center
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Refinement With Propagation
• In some situation, the perceptually meaningful regions are less than the cluster number.
• That is, some regions, which should belong to one cluster, will be grouped into several clusters.
R 139
G 160
B 127
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Refinement With Propagation (cont.)
• If one cluster is over-segmented into several subclusters, the compactness may be highly distorted.
• Thus PageRank algorithm is proposed to propagate saliency between similar clusters.
• Original PageRank algorithm
• Question : How the original PageRank come from?
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Refinement With Propagation (cont.)
• Idea : A page linked by many pages with high PageRank receives high rank as well.
• Modified algorithm
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Refinement With Propagation (cont.)
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Experiment Result
32
Experimental DataSet and Compared Method
• Dataset
EPFL dataset [1], CMU dataset [4], MSRA dataset [46] ,Itti’s method (ITTI) [38]
• Method
Spectral residual method (SR) [37]
Graph-based saliency method (GB) [36]
Frequency-tuned method (FT) [37]
Method based on color and orientation distributions (COD) [32]
Region contrast method (RC) [12] (Region-based)
Context-based method (CB) [39] (Region-based)
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Experiment Result
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Experiment Result (cont.)
• Linear Correlation Coefficient
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Experiment Result (cont.)
36
Conclusion
37
Conclusion
• The paper proposes a promising saliency detection approach, which can generate accurate saliency maps with well-defined object boundary.
• Mean shift, GMM are used to extract meaningful superpixel.
• Saliency value is refined as well with a modified PageRank algorithm.
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