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

Multiresolution visual enhancement of hazy underwater scene

In recent years there has been a growing interest in inferring social relations amongst actors in a video using audiovisual features, co-appearance features or both. The discovered relations between actors have been used for identifying leading roles, detecting rival communities in a movie plot etc. In this paper we propose an unsupervised method which uses the video’s transcript and closed caption information for discovering actor communities (group of actors or characters in a film that share a common perspective/viewpoint on an issue) from videos. The method proposed groups together actors using a topic model based approach, which jointly models actor-actor interaction (two actors interact when they share the same scene) and the topics associated with their conversations/dialogs. This joint modeling approach shows encouraging results compared to existing methods.

Inferring actor communities from videos

This paper presents a new approach to build distributed digital libraries with multimedia contents. The authors propose a new scheme for media feature based concept modelling to address the limitation of traditional ontology based multimedia retrieval systems. The perceptual models can be used for semantic query processing using standard MPEG-7 media content descriptions. The authors have defined a new ontology language M-OWL (multimedia web ontology language) to support this perceptual modelling. M-OWL is an extension to the OWL (web ontology language) with new constructs for formal representation of the media properties of the domain concepts. It supports probabilistic evidential reasoning for robust concept recognition in multimedia documents. The separation of perceptual modelling of concepts from the repository architecture enables seamless integration of diverse multimedia contents. SOA (Service Oriented Architecture) is used to integrate large number of distributed information sources, each of which is modelled as an intelligent information agent. The authors have demonstrated the capability of the architecture by building a few research prototypes, namely a virtual encyclopaedia of Indian culture, a document image repository and a multimedia portal.

Using ontology for building distributed digital libraries with multimedia contents

In this paper, we consider the problem of designing a CBIR (content based image retrieval) system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. We have designed novel query processing schemes for image retrieval based upon this representation. We have also shown applicablity of relevance feedback based learning scheme for processing multi-example based queries. Extensive experimental results with facial and natural image databases have validated effectiveness of our approach.

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Multi Example Based Image Retrieval: An ICA Based Approach.

The problem of detecting correspondences between object contours belonging to successive image frames of a dynamic scene is addressed. The object contours are represented using concave, convex, and straight line segments which are extracted after smoothing the contour with a Gaussian kernel. The matching problem is tackled using heuristic search with a state space formulation. An admissible heuristic function is designed for guiding the search. Experimental results are presented to demonstrate the capabilities of the method.

Santanu Chaudhury

Papers

Multiresolution visual enhancement of hazy underwater scene

Inferring actor communities from videos

Using ontology for building distributed digital libraries with multimedia contents

Multi Example Based Image Retrieval: An ICA Based Approach.

Heuristic search approach to shape matching in image sequences