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Histogram clustering for unsupervised segmentation and image retrieval

Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann
1999 Pattern Recognition Letters  
We present a prototypical application of this method for unsupervised segmentation of textured images based on local distributions of Gabor coefficients.  ...  This paper introduces a novel statistical latent class model for probabilistic grouping of distributional and histogram data.  ...  Acknowledgements It is a pleasure to thank Hans du Buf for providing the aerial image mixtures in  ... 
doi:10.1016/s0167-8655(99)00056-2 fatcat:gkcwzowcyfbulcpplixy6oyyna

Discrete Mixture Models for Unsupervised Image Segmentation [chapter]

Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann
1998 Mustererkennung 1998  
We demonstrate an application of this method to the unsupervised segmentation of textured images based on local empirical distributions of Gabor coe cients.  ...  This paper introduces a novel statistical mixture model for probabilistic clustering of histogram data and, more generally, for the analysis of discrete co occurrence data.  ...  for unsupervised texture segmentation.  ... 
doi:10.1007/978-3-642-72282-0_13 dblp:conf/dagm/PuzichaBH98 fatcat:fm4jlfi6l5h45ip6ha4f6xv5ju

Image Segmentation Using Markov Random Field Model in Fully Parallel Cellular Network Architectures

Tamás Szirányi, Josiane Zerubia, LászLó Czúni, David Geldreich, Zoltán Kato
2000 Real-time imaging  
In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested dierent monogrid and multigrid architectures.  ...  As an example, we have developed a simpli®ed statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNN-UM), which is a new image processing tool, containing  ...  Acknowledgements This work was partially supported by``Balaton'' programme of the National Committee for Technological Development, Hungary and the French Ministry of Foreign Aairs and by the Hungarian  ... 
doi:10.1006/rtim.1998.0159 fatcat:fr7fdhjl3nhsvb573zla6wzlqm

Unsupervised texture segmentation in a deterministic annealing framework

T. Hofmann, J. Puzicha, J.M. Buhmann
1998 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We apply both annealing variants to Brodatz like micro texture mixtures and real word images. T. Hofmann, J. Puzicha, J.M. Buhmann: Unsupervised Texture Segmentation 1  ...  We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity.  ...  Clustering of Proximity Data As we have pointed out, unsupervised segmentation is essentially a partitioning or clustering problem of labeling a set of image sites with group or texture labels l 1 K.  ... 
doi:10.1109/34.709593 fatcat:ohmp67gezvfx5nfewhsrfhpctm

MRF Based Image Segmentation Augmented with Domain Specific Information [chapter]

Özge Öztimur Karadağ, Fatoş T. Yarman Vural
2013 Lecture Notes in Computer Science  
The type of information and its representation depends on the content of the image dataset to be segmented. This information is integrated to the segmentation process in an unsupervised framework.  ...  The proposed system is compared with the state of the art unsupervised image segmentation methods quantitatively via two evaluation metrics; consistency error and probabilistic rand index and satisfactory  ...  A second group of studies, takes an unsupervised approach and construct a relatively simpler energy function. The major difference between these two approaches is the availability of labels.  ... 
doi:10.1007/978-3-642-41184-7_7 fatcat:2ivpor6sszhctldtvfos72lkny

Unsupervised Bayesian wavelet domain segmentation using Potts-Markov random field modeling

Ali Mohammad-Djafari
2005 Journal of Electronic Imaging (JEI)  
This model performs a fully unsupervised segmentation, on images composed of homogeneous regions, by introducing a hidden Markov Model (HMM) for the regions to be classified and Gaussian distributions  ...  for the noise and for the pixels pertaining to each regions.  ...  Acknowledgments The authors are very grateful to both anonymous reviewers for their attentive and constructive remarks that helped improve the quality of the presentation.  ... 
doi:10.1117/1.2139967 fatcat:27ow4ynw2fcf7ku22ss7qgzxua

Path-based clustering for grouping of smooth curves and texture segmentation

B. Fischer, J.M. Buhmann
2003 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The generality of the method is emphasized by results from grouping textured images with texture gradients in an unsupervised fashion.  ...  This grouping principle yields superior clustering results when objects are distributed on lowdimensional extended manifolds in a feature space, and not as local point clouds.  ...  The results of Pairwise Data Clustering and Histogram Clustering were reached by multiscale annealing techniques and the Normalized Cut segmentation was found with spectral analysis.  ... 
doi:10.1109/tpami.2003.1190577 fatcat:xr4hmgfnufbtvktwzvpz3xej44

Color and Texture Segmentation Using an Unified MRF Model

Sucheta Panda, Pradipta Kumar Nanda
2022 Journal of Computer and Communications  
The color image segmentation problem has been formulated in an unsupervised framework.  ...  The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation.  ...  An unsupervised color segmentation algorithm has been proposed [17] using "multiscale texture model".  ... 
doi:10.4236/jcc.2022.106012 fatcat:v5my43m2zjaflpovzhjdb7hkya

Image recognition: Visual grouping, recognition, and learning

J. M. Buhmann, J. Malik, P. Perona
1999 Proceedings of the National Academy of Sciences of the United States of America  
We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the  ...  Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint.  ...  Image analysis is inherently multiscale; segmentation, grouping, or classification have to be performed at the appropriate scales of resolution.  ... 
doi:10.1073/pnas.96.25.14203 pmid:10588681 pmcid:PMC33948 fatcat:kgku35qm4zhg7ha5d2qa2pfca4

Stochastic relaxation on partitions with connected components and its application to image segmentation

Jia-Ping Wang
1998 IEEE Transactions on Pattern Analysis and Machine Intelligence  
second type are large but complex; second, we give algorithms which are not computationally costly, for probability simulation and simulated annealing on such spaces using the neighborhoods.  ...  We present a new method of segmentation in which images are segmented by partitions with connected components.  ...  Azencott and L. Younes for valuable guidance, discussions and encouragement throughout this work.  ... 
doi:10.1109/34.683775 fatcat:mgaalm55mng5rpqy7yvxmnv46e

A Non-Iterative Approach To Initial Region Estimation Applied To Color Image Segmentation

C. Alberola-Lopez, J. Portillo-Garcia, L.J. Tardon-Garcia, J.I. Trueba-Santader
1996 Zenodo  
The general problem of texture segmentation has received much attention in the last decades, and there is not a nal general solution for the time being.  ...  INTRODUCTION In this paper we address the problem of texture segmentation by statistical decision and classi cation.  ... 
doi:10.5281/zenodo.36103 fatcat:rgyuixmezjf3xoeu6zc2va2vh4

Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations [article]

Alan Dolhasz, Carlo Harvey, Ian Williams
2020 arXiv   pre-print
Many tasks in computer vision are often calibrated and evaluated relative to human perception.  ...  Specifically, we present a novel methodology for learning to detect image transformations visible to human observers through approximating perceptual thresholds.  ...  The minimum and maximum learning rate in the annealing schedule are set to 1e−6 and 1e−4, respectively.  ... 
arXiv:1912.06433v3 fatcat:d65czvcvcra7lkux3nyx4girxu

A multiscale random field model for Bayesian image segmentation

C.A. Bouman, M. Shapiro
1994 IEEE Transactions on Image Processing  
We also develop a computationally efficient method for unsupervised estimation of model parameters.  ...  Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing.  ...  For the texture segmentation problem, a stochastic texture model can be used for p y|x (y|x) [4, 35, 15] , or texture feature vectors can be extracted at each pixel [36, 37, 38] and modeled with a multivariate  ... 
doi:10.1109/83.277898 pmid:18291917 fatcat:r4tnzihsfzeddktnnm37sio7d4

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Tamás Czimmermann, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo, Paolo Dario
2020 Sensors  
Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.  ...  We continue with a survey of textural defect detection based on statistical, structural and other approaches.  ...  SpA, Robot System Automation srl, Roggi srl and Robotech srl. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20051459 pmid:32155900 fatcat:rsdnszztffbadllniclol3pjvi

A Brief Survey of Color Image Preprocessing and Segmentation Techniques

Siddhartha Bhattacharyya
2011 Journal of Pattern Recognition Research  
This article presents a brief survey of the aforestated trends in color image enhancement and segmentation.  ...  The non-classical approaches comprising the neuro-fuzzy-genetic paradigm or its variants are bestowed with features for real time applications.  ...  Luo and Khoshgoftaar [27] applied the MS clustering method for designing an unsupervised multiscale color image segmentation algorithm.  ... 
doi:10.13176/11.191 fatcat:iy4md7fp3rc37osn6qpgkuviqm
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