Author
Santanu Chaudhury
Other affiliations: Central Electronics Engineering Research Institute, Indian Institute of Technology Delhi, Indian Statistical Institute ...read more
Bio: Santanu Chaudhury is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Ontology (information science) & Image segmentation. The author has an hindex of 28, co-authored 380 publications receiving 3691 citations. Previous affiliations of Santanu Chaudhury include Central Electronics Engineering Research Institute & Indian Institute of Technology Delhi.
Papers published on a yearly basis
Papers
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16 Apr 2015TL;DR: An approach for view classification, Spatial Pyramid Histogram of Words which successfully models the appearance and shape distributions of object class and shows a classification accuracy of 98.3% on an exhaustive database of 703 ultrasound images.
Abstract: When imaging the heart, using a 2D ultrasound probe, different views can manifest depending on the location and angulations of the probe. Some of these views have been labeled as standard views, due to the presentation and ease of assessment of key cardiac structures in them. We present an approach for automatic recognition and classification of these standard views, as a potential enabler for automated measurements or detection of noise — all without a human in the loop. We present an approach for view classification, Spatial Pyramid Histogram of Words which successfully models the appearance and shape distributions of object class. We demonstrate the effectiveness of this technique for the task of discrimination between the B-mode Parasternal Long Axis (PLAX) and the Short Axis (SAX) echocardiograms. For this task, our method shows a classification accuracy of 98.3% on an exhaustive database of 703 ultrasound images.
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19 Dec 2016TL;DR: This paper presents a multi-modal document image retrieval framework by learning an optimal fusion of information from text and info-graphics regions and demonstrates the evaluation of the proposed concept on documents collected from various sources.
Abstract: Information retrieval research has shown significant improvement and provided techniques that retrieve documents in image or text form. However, retrieval of multi-modal documents has been given very less attention. We aim to build a system for retrieval of documents with embedded information graphics (Info-graphics). Info-graphics are images of bar charts and line graphs appearing with textual components in magazines, newspapers, and journals. In this paper, we present multi-modal document image retrieval framework by learning an optimal fusion of information from text and info-graphics regions. The evaluation of the proposed concept is demonstrated on documents collected from various sources such as magazines and journals.
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01 Dec 2015TL;DR: This work identified the fact that correct frame can be represented precisely in terms of dictionary atoms but while representing a distorted frame, the error drastically increases with increase in distortion thus it can easily classify the frames as correct and distorted based on error score calculated by sparse representation framework.
Abstract: This paper describes a sparse representation based approach to learn a classifier for assessing the video quality without a reference. First we calculate the natural scene statistics (NSS) based spatial features of each frame/image and then learn a dictionary by K-SVD algorithm from NSS features of correct frames. In this work we identified the fact that correct frame can be represented precisely in terms of dictionary atoms but while representing a distorted frame, the error drastically increases with increase in distortion thus we can easily classify the frames as correct and distorted based on error score calculated by sparse representation framework. This framework has been validated on two datasets and we observe improved accuracies as compared to state-of-art algorithms.
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TL;DR: In this paper, a new Multimedia Web Ontology Language (E-MOWL) is presented to handle events with media depictions, where the temporal, spatial and entity aspects that are implicitly linked to an event are represented through this language to model the context of events.
Abstract: This paper presents formalization of a new Multimedia Web Ontology Language (E-MOWL) to handle events with media depictions. The temporal, spatial and entity aspects that are implicitly linked to an event are represented through this language to model the context of events. The already existing Multimedia Web Ontology Language (MOWL) can be leveraged for perceptual modelling of a domain, where the concepts manifest into media patterns in the multimedia document and helps in semantic processing of the contents. The language E-MOWL provides a rich method for representing knowledge corresponding to a specific domain wherein the context specifies the intended meaning of each element of the domain of discourse; an element in different context may correspond to different functional role. The context information associated with an event ties the audiovisual data with event related aspects. All these aspects when considered altogether provide the evidence and contribute towards recognizing an event from multimedia documents. The language also enables reasoning with the uncertainty associated with the events and is organized in the form of Bayesian Network (BN). The media items that are semantically relevant can be assimilated together on the basis of their association with events. We have demonstrated the efficacy of our approach by utilizing an ontology for the entertainment category in news domain to offer an application \textit{news aggregation} and event-based book recommendations.
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05 Dec 2017TL;DR: A novel framework is applied on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.
Abstract: In today’s competitive telecommunication industry understanding the causes that influence the revenue is of importance. In a continuously evolving business environment, the causes that influence the revenue keeps changing. To understand and quantify the effect of different factors we model it as a non-stationary temporal causal network. To handle the massive volume of data, we propose a novel framework as part of which we define rules to identify the concept drift and propose an incremental algorithm for learning non-stationary temporal causal structure from streaming data. We apply the framework on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.
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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
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3,940 citations
01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Abstract: 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.
3,627 citations
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TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Abstract: 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.
2,653 citations