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

This paper proposes a new method for using implicit user feedback from clickthrough data to provide personalized ranking of results in a video retrieval system. The annotation based search is complemented with a feature based ranking in our approach. The ranking algorithm uses belief revision in a Bayesian Network, which is derived from a multimedia ontology that captures the probabilistic association of a concept with expected video features. We have developed a content model for videos using discrete feature states to enable Bayesian reasoning and to alleviate on-line feature processing overheads. We propose a reinforcement learning algorithm for the parameters of the Bayesian Network with the implicit feedback obtained from the clickthrough data.

Learning ontology for personalized video retrieval

Large space with many cameras require huge storage and computational power to process these data for surveillance applications. In this paper we propose a distributed camera and processing based face detection and recognition system which can generate information for finding spatiotemporal movement pattern of individuals over a large monitored space. The system is built upon Hadoop Distributed File System using map reduce programming model. A novel key generation scheme using distance based hashing technique has been used for distribution of the face matching task. Experimental results have established effectiveness of the technique.

Monitoring a large surveillance space through distributed face matching

This paper presents a context-aware ontology driven approach to water resource management in smart cities for providing adequate water supply to the citizens. The appropriate management of water requires exploitation of efficient action plan to review the prevailing causes of water shortage in a geospatial environment. This involves analysis of historical and real-time water specific information captured through heterogeneous sensors. Since the gathered contextual data is available in different formats so interoperability across diverse data requires converting it into a common perceivable RDF format. As the perceptual model of the Smart Water domain comprises of observable media properties of the concepts so to achieve context-aware data fusion we have employed multimedia ontology based semantic mapping. The multimedia ontology encoded in Multimedia Web Ontology Language (MOWL) forms the core of our IoT based smart water application. It supports Dynamic Bayesian Network based probabilistic reasoning to predict the changing situations in a real-time irregular environment patterns. Ultimately, the paper presents a context-aware approach to deal with uncertainties in water resource in the face of environment variability and offer timely conveyance to water authorities by circulating warnings via text-messages or emails. To illustrate the usability of the presented approach we have utilized the online available sample water data-sets.

Smart Water Management: An Ontology-Driven Context-Aware IoT Application

Cryptography is the backbone for the security systems. The main challenge in use of the Cryptosystems is maintaining the confidentiality of the cryptographic key. A Cryptosystem which encrypts the data using biometric features improves the security of the data and overcomes the problems of key management and key confidentiality. Fuzzy Vault Scheme proposed by Juels and Sudan [1] binds the secret key and the biometric template, so that extraction of the secret without the biometric data is infeasible. Physical signature is a biometric that is widely accepted and is used for proving the authenticity of a person in legal documents, bank transactions etc. Electronic devices such as digital tablets capture azimuth, altitude and pressure along with x any y coordinates at fixed time interval. This paper describes a robust online signature based cryptosystem to hide the secret by binding it with invariant online signature templates. The invariant templates of the signature are derived from artificial neural network based classifier. The entire signature is divided into fixed number of time slices. Important features are extracted based on the consistency of the feature in the slices of the genuine signature. Binary back propagation based neural network for each feature, each subset of slices for a user is trained by a weighted back propagation algorithm. The decisions of these networks are combined using AdaBoost algorithm. The proposed scheme is highly robust as it works well for all kinds of signatures and is independent of the number of zero crossing and high curvature points in the signature trajectory.

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Santanu Chaudhury

Papers

Learning ontology for personalized video retrieval

Monitoring a large surveillance space through distributed face matching

Smart Water Management: An Ontology-Driven Context-Aware IoT Application

A Robust Online Signature Based Cryptosystem

Off-line hand written input based identity determination using multi kernel feature combination