Ten lectures on wavelets
Segmenting focused objects in complex visual images
Pattern Recognition Letters
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
Digital Image Processing
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Scientific Inside
Digital Image Processing: PIKS Scientific Inside
Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Automatic image segmentation by dynamic region growth and multiresolution merging
IEEE Transactions on Image Processing
Image segmentation by iterated region merging with localized graph cuts
Pattern Recognition
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmenting a low-depth-of-field image using morphological filters and region merging
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Unsupervized Video Segmentation With Low Depth of Field
IEEE Transactions on Circuits and Systems for Video Technology
Segmenting focused objects based on the Amplitude Decomposition Model
Pattern Recognition Letters
Learning to Extract Focused Objects From Low DOF Images
IEEE Transactions on Circuits and Systems for Video Technology
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
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In this paper, a two-stage unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low depth-of-field (DOF) images. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks closely conforming to image objects are extracted. In stage two, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the region-of-interest (ROI) from the map. Experimental results demonstrate that the proposed approach achieves an F-measure of 91.3% and is computationally 3 times faster than the existing state-of-the-art approach.