Floating search methods in feature selection
Pattern Recognition Letters
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy
Pattern Recognition Letters
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic image segmentation by dynamic region growth and multiresolution merging
IEEE Transactions on Image Processing
Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor
IEEE Transactions on Image Processing
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral clustering with fuzzy similarity measure
Digital Signal Processing
Entropy rate superpixel segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Quantum-Inspired Immune Clonal Algorithm for Global Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Saliency filters: Contrast based filtering for salient region detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Context-Aware Saliency Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy spectral clustering with robust spatial information for image segmentation
Applied Soft Computing
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In this paper, a novel automatic image segmentation method is proposed. To extract the foreground of the image automatically, we combine the region saliency based on entropy rate superpixel (RSBERS) with the affinity propagation clustering algorithm to get seeds in an unsupervised manner, and use random walks method to obtain the segmentation results. The RSBERS first applies entropy rate superpixel segmentation method to split the image into compact, homogeneous and similar-sized regions, and gets the saliency map by applying saliency estimation methods in each superpixel regions. Then, in each saliency region, we apply the affinity propagation clustering to extract the representative pixels and obtain the seeds. A relabeling strategy is presented to ensure the extracted seeds inside the expected object. Additionally, in order to enhance the effects of segmentation, a new feature descriptor is designed using the covariance matrices of coordinates, color and texture information. Experiments on publicly available data sets demonstrate the excellent segmentation performance of our proposed method.