A connectionist approach for clustering with applications in image analysis
01 Mar 1994-Vol. 24, Iss: 3, pp 365-384
TL;DR: A new neural network strategy for clustering that works on the histogram and is therefore faster than existing unsupervised learning networks and was applied to a wide class of problems including gray level image reduction, color segmentation and remotely sensed image segmentation.
Abstract: A new neural network strategy for clustering is presented. The network works on the histogram and the process is similar to mode separation. The number of clusters are autonomously detected by the network and it overcomes some major difficulties encountered by mode separation techniques. Clustering is done by first selecting the prototypes and then assigning patterns to one of the prototypes based on its distance from the prototype and the distribution of data. The network does not employ weight learning and is therefore faster than existing unsupervised learning networks. The network was applied to a wide class of problems including gray level image reduction, color segmentation and remotely sensed image segmentation. The experimental results obtained are promising. >
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TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
14,054 citations
Patent•
30 Oct 1995TL;DR: In this article, the histogram is divided into clusters using a pattern matching technique and then histogram equalization or stretching is performed on each cluster to produce a modified histogram.
Abstract: A method of operating a computer to produce contrast enhanced digital images commences with the step of producing a histogram of having a first axis corresponding to a measurable property (e.g., luminance) and a second axis corresponding to a count of pixels having a particular value for the measurable property. This histogram is divided into clusters and histogram equalization or stretching is performed on each cluster thereby producing a modified histogram. Using said modified histogram to adjust the value of said first measurable property in said digital form, thereby producing a contrast enhanced image. The histogram is divided into clusters using a pattern matching technique. For example, patterns in the histogram that resemble gaussian distributions and patterns that resemble uniform distributions are separated into individual clusters.
83 citations
TL;DR: A comparison of the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning and three hierarchical cluster methods in 2,580 data sets with known cluster structure finds the performance of the Kohonen networks was similar to, or better than, the other methods.
Abstract: Neural Network models are commonly used for cluster analysis in engineering, computational neuroscience, and the biological sciences, although they are rarely used in the social sciences. In this study we compare the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning (Hartigan and Spathk-means) and three hierarchical (Ward's, complete linkage, and average linkage) cluster methods in 2,580 data sets with known cluster structure. Overall, the performance of the Kohonen networks was similar to, or better than, the performance of the other methods.
73 citations
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...Interestingly, however, a combined search with these keywords produced two citations only (Atiya, 1990; Vinod, Chaudhury, Mukherjee, & Ghose, 1994 ), neither of which were published in a psychology journal....
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TL;DR: Techniques for automatic partitioning and labeling SOM networks in clusters of neurons that may be used to represent the data clusters are proposed.
Abstract: Clustering is the process of discovering groups within the data, based on similarities, with a minimal, if any, knowledge of their structure. The self-organizing (or Kohonen) map (SOM) is one of the best known neural network algorithms. It has been widely studied as a software tool for visualization of high-dimensional data. Important features include information compression while preserving topological and metric relationship of the primary data items. Although Kohonen maps had been applied for clustering data, usually the researcher sets the number of neurons equal to the expected number of clusters, or manually segments a two-dimensional map using some a-priori knowledge of the data. This paper proposes techniques for automatic partitioning and labeling SOM networks in clusters of neurons that may be used to represent the data clusters. Mathematical morphology operations, such as watershed, are performed on the U-matrix, which is a neuron-distance image. The direct application of watershed leads to an oversegmented image. It is used markers to identify significant clusters and homotopy modification to suppress the others. Markers are automatically found by performing a multilevel scan of connected regions of the U-matrix. Each cluster of neurons is a sub-graph that defines, in the input space, complex and non-parametric geometries which approximately describes the shape of the clusters. The process of map partitioning is extended recursively. Each cluster of neurons gives rise to a new map, which are trained with the subset of data that were classified to it. The algorithm produces dynamically a hierarchical tree of maps, which explains the cluster's structure in levels of granularity. The distributed and multiple prototypes cluster representation enables the discoveries of clusters even in the case when we have two or more non-separable pattern classes.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
47 citations
TL;DR: An exploratory methodology for discriminating zoom-in, zoom-out, and no-zoom intent was developed for such applications as telerobotics, disability aids, weapons systems, and process control interfaces, and has broader potential for discrimination of user intent in other interface operations.
Abstract: Discrimination of user intent at the computer interface solely from eye gaze can provide a powerful tool, benefiting many applications. An exploratory methodology for discriminating zoom-in, zoom-out, and no-zoom intent was developed for such applications as telerobotics, disability aids, weapons systems, and process control interfaces. Using an eye-tracking system, real-time eye-gaze locations on a display are collected. Using off-line procedures, these data are clustered, using minimum spanning tree representations, and then characterized. The cluster characteristics are fed into a multiple linear discriminant analysis, which attempts to discriminate the zoom-in, zoom-out, and no-zoom conditions. The methodologies, algorithms, and experimental data collection procedure are described, followed by example output from the analysis programs. Although developed specifically for the discrimination of zoom conditions, the methodology has broader potential for discrimination of user intent in other interface operations.
46 citations
References
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TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Abstract: Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
16,652 citations
Book•
01 Jan 1991TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Abstract: From the Publisher:
This book is a comprehensive introduction to the neural network models currently under intensive study for computational applications. It is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
7,518 citations
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Abstract: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons. The content- addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological "neurons." Collective analog electrical circuits of the kind described will certainly function. The collective states of the two models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.
6,042 citations
Book•
01 Jul 1988
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Abstract: A model for a large network of "neurons" with a graded response (or sigmoid input--output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons. The content-addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological "neurons." Collective analog electrical circuits of the kind described will certainly function. The collective states of the two models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.
5,734 citations