Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Active learning and crowd sourcing are becoming increasingly popular in the machine learning community for fast and cost effective generation of labels for large volumes of data. However, such labels may be noisy. So, it becomes important to ignore the noisy labels for building of a good classifier. We propose a framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples. The approach inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance. © 2013 IEEE.

World University root node

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Greedy search for active learning of OCR

Events in multimedia objects represent landmarks of a story encoded in the media. This paper proposes a new specification for representing Events using MOWL (E-MOWL). Using this approach, higher-level complex events are detected based on the hierarchy of events present in the Event Ontology created using MOWL. The MOWL definition of Spatio-Temporal relations is extended to represent the Context of an event. The Context is any kind of information which gets defined with respect to an event instance wherein Context has its own structure and involves parameters like Time, Geographical location, and Actors/Entities involved in the event. Such an extension would enhance event detection and facilitate complete semantics, besides providing scope for personalized multimedia information retrieval. © 2013 IEEE.

Proceedings - 2013 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IATW 2013

Extending MOWL for Event Representation (E-MOWL)

A face quality metric must quantitatively measure the usability of an image as a biometric sample. Though it is well established that quality measures are an integral part of robust face recognition systems, automatic measurement of bio-metric quality in face is still challenging. Inspired by scene recognition research, this paper investigates the use of holistic super-ordinate representations, namely, Gist and sparsely pooled Histogram of Orientated Gradient (HOG), in classifying images into different quality categories that are derived from matching performance. The experiments on the CAS-PEAL and SCFace databases containing covariates such as illumination, expression, pose, low-resolution and occlusion by accessories, suggest that the proposed algorithm can efficiently classify input face image into relevant quality categories and be utilized in face recognition systems. © 2013 IEEE.

2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Can holistic representations be used for face biometric quality assessment?

We address the problem of automatic image annotation in large vocabulary datasets. In such datasets, for a given label, there could be several other labels that act as its confusing labels. Three possible factors for this are (i) incomplete-labeling ("cars" vs. "vehicle"), (ii) label-ambiguity ("flowers" vs. "blooms"), and (iii) structural-overlap ("lion" vs. "tiger"). While previous studies in this domain have mostly focused on nearest-neighbour based models, we show that even the conventional one-vs-rest SVM significantly outperforms several benchmark models. We also demonstrate that with a simple modification in the hinge-loss of SVM, it is possible to significantly improve its performance. In particular, we introduce a tolerance-parameter in the hinge-loss. This makes the new model more tolerant against the errors in the classification of samples tagged with confusing labels as compared to other samples. This tolerance parameter is automatically determined using visual similarity and dataset statistics. Experimental evaluations demonstrate that our method (referred to as SVM with Variable Tolerance or SVM-VT) shows promising results on the task of image annotation on three challenging datasets, and establishes a baseline for such models in this domain.

BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013

Exploring SVM for image annotation in presence of confusing labels

The Z-number, proposed by Zadeh in the year 2011, is a new fuzzy-theoretic approach to the Computing With Words (CWW) paradigm. It aspires to capture the uncertainty of information conveyed by a sentence, and serve as a model for the precisiation and linguistic summarization of a natural language statement. The Z-number thereby, lends a new dimension to CWW - uniting CWW with Natural Language Processing (NLP). This article is an illumination upon our exploration of the Z-number approach to CWW. Here, we enlist the probable contributions of the Z-number to CWW, present our algorithm for CWW using the Z-number, and describe a simulation of the technique with respect to a real-life example of CWW. In the course of the simulation, we extend the interpretation of the set-theoretic intersection operator to evaluate the intersection of perceptions and discover some of the challenges underlying the implementation of the Z-number in the area of CWW. © 2013 Springer-Verlag Berlin Heidelberg.

Studies in Fuzziness and Soft Computing

The Z-number enigma: A study through an experiment

In the course of comprehension, the human brain makes astounding inferences despite insufficient and ambiguous data - the data mostly being 'words' that frame sentences. Can this neural process of "abstraction of the relevant" be sufficiently modeled such that the computer may be induced to perform the same? This article is our response to this very question; a treatise on our attempt at engineering the basis of such a model, or rather, a methodology of 'relevance cognition'. Drawing inspiration from the way children learn languages, we propose here a corpus and entropy-based methodology that follows a subjectivist approach to granulate and identify statements of consequence in a natural language sample, all within the periphery of a particular context. Besides promoting language grasping abilities of a machine-through simulation of the intuitive process of relevant-information segregation-the methodology aims at reducing overall text-processing costs. The suggested scheme considers 'conceptual keywords' to be the basis of sentential understanding and utilizes the principles of Bayes' conditional probability and Shannon's entropy theorem. Experimental results have been provided to substantiate our logic. Though this article has been formulated over the backdrop of the Z-number approach to computing with words (CWW), it nevertheless applies to research areas in natural language processing (NLP) like text summarization, semantic disambiguation and concept graph generation. © 2013 Elsevier B.V. All rights reserved.

Fulltext

Theoretical Computer Science

Context granulation and subjective-information quantification

Several multimedia applications need to reason with concepts and their media properties in specific domain contexts. Media properties of concepts exhibit some unique characteristics that cannot be dealt with conceptual modeling schemes followed in the existing ontology representation and reasoning schemes. We have proposed a new perceptual modeling technique for reasoning with media properties observed in multimedia instances and the latent concepts. Our knowledge representation scheme uses a causal model of the world where concepts manifest in media properties with uncertainties. We introduce a probabilistic reasoning scheme for belief propagation across domain concepts through observation of media properties. In order to support the perceptual modeling and reasoning paradigm, we propose a new ontology language, Multimedia Web Ontology Language (MOWL). Our primary contribution in this article is to establish the need for the new ontology language and to introduce the semantics of its novel language constructs. We establish the generality of our approach with two disperate knowledge-intensive applications involving reasoning with media properties of concepts. © 2013 ACM.

ACM Transactions on Multimedia Computing, Communications and Applications

MOWL: An ontology representation language for web-based multimedia applications

The past few decades have witnessed an intensive research on optical character recognition (OCR) for Roman, Chinese, and Japanese scripts. A lot of work has been also reported on OCR efforts for various Indian scripts, like Devanagari, Bangla, Oriya, Tamil, Telugu, Malayalam, Kannada, Gurmukhi, Gujarati, etc. In this paper, we present a review of OCR work on Indian scripts, mainly on Bangla and Devanagari-the two most popular scripts in India. We have summarized most of the published papers on this topic and have also analysed the various methodologies and their reported results. Future directions of research in OCR for Indian scripts have been also given. © 2013 Indian Academy of Sciences.

Sadhana - Academy Proceedings in Engineering Sciences

A survey on optical character recognition for Bangla and Devanagari scripts

Pose estimation of cylindrical pellet using a single camera-in-hand configuration of a robot is discussed in this paper. Approaches to estimate pose in both isolated and an occluded environment is discussed. The pellet contour from the segmented image of the scene was compared with contours in the database to ascertain the matching orientation. For occluded pellets, a multiple-view based pose recognition system is proposed. Later, the estimated pose was communicated to the robot to enable it to pick-up the pellet. This has been experimentally implemented for cylindrical pellets and the performance is discussed. The algorithm enables online pellet pose determination and pick-up using KUKA KR5 robot. © 2013 ACM.

Department of Mechanical Engineering

ACM International Conference Proceeding Series

Cylindrical pellet pose estimation in clutter using a single robot mounted camera

This paper presents a genre-specific modeling strategy capable of improving the task of content based video classification and the speed of data retrieval operations. With the ever increasing growth of video data it is important to classify video shots into groups based on its content. For that reason, it is of primary concern to design systems that could automatically classify videos into different genres based on its content. We consider the genre recognition task as a classification problem. We use support vector machines to perform the classification task and propose an improved video classification method. The experimental results show that genre-specific modeling of features can significantly improve the performance. Results have been compared with two contemporary works on video classification, to demonstrate the superiority of our proposed framework. © 2013, Springer-Verlag London.