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.

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Data clustering: a review

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

Distributed fuzzy case based reasoning

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. >

A connectionist approach for clustering with applications in image analysis

In this paper we propose an evolutionary learning based fuzzy theoretic approach for classifying video sequences into generic categories. This categorization is based on video structure based syntactic features. The features like shot durations, editing style, camera work and shot activity conveys large amount of information about the type of video. The information derived from these features is integrated over a larger time-scale than a shot length time to form fuzzy rules for the extraction of video structure based features. Evolutionary learning paradigm is used to evolve the fuzzy rule based system for generic video characterization. Such a rule-based system yields high representational accuracy of the classes as shown by the experiments conducted on various type of video sequences ranging up to 1 to 3 minutes. Experimental analysis illustrates the effectiveness of our system in offering a novel approach for categorizing the video sequences.

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Generic Video Classification: An Evolutionary Learning Based Fuzzy Theoretic Approach.

In this paper an improved, macroblock (MB) level, visual saliency algorithm, aimed at video compression, is presented. A Relevance Vector Machine (RVM) is trained over 3 dimensional feature vectors, pertaining to global, local and rarity measures of conspicuity, to yield probabalistic values which form the saliency map. These saliency values are used for non-uniform bit-allocation over video frames. A video compression architecture for propagation of saliency values, saving tremendous amount of computation, is also proposed.

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A scheme for attentional video compression

Analysis of stomata density and its configuration based on scanning electron microscopic (SEM) image of a leaf surface, is an effective way to characterize the plant’s behaviour under various environmental stresses (drought, salinity etc.). Existing methods for phenotyping these stomatal traits are often based on manual or semi-automatic labeling and segmentation of SEM images. This is a low-throughput process when large number of SEM images is investigated for statistical analysis. To overcome this limitation, we propose a novel automated pipeline leveraging deep convolutional neural networks for stomata detection and its quantification. The proposed framework shows a superior performance in contrast to the existing stomata detection methods in terms of precision and recall, 0.91 and 0.89 respectively. Furthermore, the morphological traits (i.e. length & width) obtained at stomata quantification step shows a correlation of 0.95 and 0.91 with manually computed traits, resulting in an efficient and high-throughput solution for stomata phenotyping.

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Santanu Chaudhury

Papers

Distributed fuzzy case based reasoning

A connectionist approach for clustering with applications in image analysis

Generic Video Classification: An Evolutionary Learning Based Fuzzy Theoretic Approach.

A scheme for attentional video compression

Deep Convolutional Neural Networks Based Framework for Estimation of Stomata Density and Structure from Microscopic Images