Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

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

A domain-specific ontology models a specific domain or part of the world. In fact, ontologies have proven to be an excellent medium for capturingpagebreak the knowledge of a domain. We propose an ontology learning scheme in this paper which combines standard multimedia analysis techniques with knowledge drawn from conceptual meta-data to learn a domain-specific multimedia ontology from a set of annotated examples. A standard machine-learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between high-level semantic concepts of the domain and low-level media features. We construct a more robust and refined version of the basic ontology by learning from this set of conceptually annotated data. We show an application of our ontology-based framework for exploration of multimedia content, in the field of cultural heritage preservation. By constructing an ontology for the cultural heritage domain of Indian classical dance, and by offering an application for semantic annotation of the heritage collection of Indian dance videos, we demonstrate the efficacy of ou approach. © 2012, Springer-Verlag London.

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International Journal of Multimedia Information Retrieval

Acquisition of multimedia ontology: an application in preservation of cultural heritage

Automatic image annotation aims at predicting a set of textual labels for an image that describe its semantics. These are usually taken from an annotation vocabulary of few hundred labels. Because of the large vocabulary, there is a high variance in the number of images corresponding to different labels ("class-imbalance"). Additionally, due to the limitations of manual annotation, a significant number of available images are not annotated with all the relevant labels ("weak-labelling"). These two issues badly affect the performance of most of the existing image annotation models. In this work, we propose 2PKNN, a two-step variant of the classical K-nearest neighbour algorithm, that addresses these two issues in the image annotation task. The first step of 2PKNN uses "image-to-label" similarities, while the second step uses "image-to-image" similarities; thus combining the benefits of both. Since the performance of nearest-neighbour based methods greatly depends on how features are compared, we also propose a metric learning framework over 2PKNN that learns weights for multiple features as well as distances together. This is done in a large margin set-up by generalizing a well-known (single-label) classification metric learning algorithm for multi-label prediction. For scalability, we implement it by alternating between stochastic sub-gradient descent and projection steps. Extensive experiments demonstrate that, though conceptually simple, 2PKNN alone performs comparable to the current state-of-the-art on three challenging image annotation datasets, and shows significant improvements after metric learning. © 2012 Springer-Verlag.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Image annotation using metric learning in semantic neighbourhoods

A wide category of objects and scenes can be effectively searched and classified using the modern descriptors and classifiers. With the performance on many popular categories becoming satisfactory, we explore into the issues associated with much harder recognition problems. We address the problem of searching specific images in Indian stone-carvings and sculptures in an unsupervised setup. For this, we introduce a new dataset of 524 images containing sculptures and carvings of eight different Indian deities and three other subjects popular in the Indian scenario. We perform a thorough analysis to investigate various challenges associated with this task. A new image-representation is proposed using a sequence of discriminative patches mined in an unsupervised manner. For each image, these patches are identified based on their ability to distinguish the given image from the image most dissimilar to it. Then a rejection-based re-ranking scheme is formulated based on both similarity as well as dissimilarity between two images. This new scheme is experimentally compared with two baselines using state-of-the-art descriptors on the proposed dataset. Empirical evaluations demonstrate that our proposed method of image-representation and rejection cascade improves the retrieval performance on this hard problem as compared to the baseline descriptors. © 2012 ACM.

ACM International Conference Proceeding Series

Neti Neti: In search of deity

Indian universities broadly follow an entrance process for undergraduate engineering process in which physics, chemistry and mathematics based questions are asked. Either the individual scores or some combination of these scores is used to set the admission criterion for different engineering streams. However, it has not been analyzed whether this is a good predictor of undergraduate performance or not, especially in Indian context. The broad goal of this research is to understand the relationship between entrance criterion and undergraduate Computer Science/Information Technology performance. The analysis includes data collected from the students who appeared in the entrance exams and took admission in IIIT-Delhi in between 2008 to 2010. The scores obtained in entrance exams and cumulative grade point average earned during the first year of their studies are analyzed. Preliminary analysis suggests that for computer science undergraduate studies, mathematics or numerical understanding is an important aspect. Further, logic and aptitude based tests are better correlated in context of computer science education. © 2012 IEEE.

Proceedings - Frontiers in Education Conference, FIE

Work in progress: On entrance test criteria for CS and IT UG programs

It is generally assumed that group studies are more effective for students than individual studies. The objective of this work in progress is to quantitatively evaluate and analyze the effect of collaborative studies on individual students performance. This effort would help the student stimulate interest in group learning and collaboration along with exposing them towards multiple problem solving approaches while working individually or in groups. This way the students are challenged to use their existing knowledge and approach, and augment it further with the knowledge and approach provided by group partners. While there are several efforts that focus on developing new group learning techniques, we intend to study the efficacy of previously proposed techniques under various test settings for EE and CS courses without significantly diverting from the course framework. © 2012 IEEE.

Work in progress: A quantitative study of effectiveness in group learning

This paper develops a production-inventory model for a deteriorating item with stock-dependent demand under two storage facilities over a random planning horizon, which is assumed to follow exponential distribution with known parameter. The effects of learning in set-up, production, selling and reduced selling is incorporated. Different inflation rates for various inventory costs and time value of money are also considered. A hybrid genetic algorithm is designed to solve the optimization problem which is hard to solve with existing algorithms due to the complexity of the decision variable. To illustrate the model and to show the effectiveness of the proposed approach a numerical example is provided. A sensitivity analysis of the optimal solution with respect to the parameters of the system is carried out. © 2010 Springer-Verlag.

Central European Journal of Operations Research

Two-warehouse production model for deteriorating inventory items with stock-dependent demand under inflation over a random planning horizon

A Cellular Automata (CA) is a computing model of complex system using simple rule. CA divides the problem space into number of cell and each cell can be one or several final state. Cells are affected by neighbors to the simple rule. This paper provides a survey of available literature of some methodologies employed by researchers to utilize the cellular automata for modeling purpose. The literature survey introduces the different type of cellular automata being used for modeling and also tries to present the different fields in which CA have been applied. © 2012 Springer-Verlag.

Communications in Computer and Information Science

A survey on cellular automata and its applications

In this paper, we present a novel technique for detection of concave regions as a structural information of character images. The problem difficulty lies in reporting all concavities irrespective of the viewing direction on the 2D plane. In our approach, we detect concave regions by analyzing the sequence of discrete turns taken to describe the character stroke; hence, it becomes view-invariant. The proposed method has the added advantage of detecting same concave regions of a particular character written by different individuals. We have tested our method on printed and handwritten Bangla and Hindi isolated character images. Initial results demonstrate the efficacy of our approach. © 2012 Springer-Verlag.

Detection of structural concavities in character images - A writer-independent approach

In this chapter, we discuss the problem of 3D shape matching. Recent advancements in shape acquisition technology has led to the capture of large amounts of 3D data. Existing real-time multi-camera 3D acquisition methods provide a frame-wise reliable visual-hull or mesh representations for real 3D animation sequences [1, 2, 3, 4, 5, 6]. The task of 3D shape analysis involves tracking, recognition, registration, etc. Analyzing 3D data in a single framework is still a challenging task considering the large variability of the data gathered with different acquisition devices. 3D shape registration is one such challenging shape analysis task. The major difficulties in shape registration arise due to: (1) variation in the shape acquisition techniques, (2) local deformations in nonrigid shapes, (3) large acquisition discrepancies (e.g., holes, topology change, surface acquisition noise), (4) local scale change. © 2012 by Taylor & Francis Group, LLC.

Image Processing and Analysis with Graphs: Theory and Practice

3D shape registration using spectral graph embedding and probabilistic matching

A framework has been proposed for representing the trajectory of a moving object, using a novel hyperstring based approach for efficient retrieval of video shots. The hyperstring based model unifies both the structural and kinematic features for an improved representation of the trajectory. A Constraint-driven Adjacency Graph matching (CAGM) algorithm has been proposed to measure the similarity between a pair of query and model hyperstrings. Experiments have been performed on benchmark datasets of trajectories (one synthetic and three real-world video shots), to assess the performance (using Precision-Recall metric) of the proposed model. Results have been compared with two similar published works on video retrieval using trajectories, to demonstrate the superiority of our proposed framework. © 2012 IEEE.