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

An automatic lookup tool, which matches and retrieves similar floorplans from a large repository of digitized architectural floorplans can prove to be of immense help for the architects while designing new projects. In this paper, we have proposed a framework for the matching and retrieval of similar architectural floorplans under the query by example paradigm. We propose a room layout segmentation and adjacent room detection algorithm to represent layouts as an undirected graph. We have also proposed a novel graph spectral embedding feature to uniquely represent the layout of the architectural floorplan. This helps in effective and efficient matching of the room layouts. Room semantics in terms of both the room structures and room decor is used to retrieve similar floorplans from the repository. To match the semantic similarity between a pair of floorplans, we have proposed a two stage matching technique. We have validated the effectiveness of our proposed framework by performing experiments on publicly available floorplan dataset and achieved high retrieval accuracy. © 2016 IEEE.

World University root node

Proceedings - International Conference on Pattern Recognition

A unified framework for semantic matching of architectural floorplans

The number of people using wireless devices and internet has increased remarkably in the previous decade, and so is the need to route/broadcast information to the entire network. One of the latest and most promising approaches is finding a Connected Dominating Set (CDS) in network. Another popular concept in the social network analysis is Node centrality, because of its ability to find popular/influential nodes. In this paper, we have formulated a simple but efficient algorithm, which first finds a CDS in the network, and then with the help of node centrality metrics it finds a central node, for more effective routing/broadcasting. We have increased simplicity by making changes in the existing algorithms, incorporated Node Centrality, and ensured its efficiency by a step-by-step complexity analysis and constant changes and improvements.

2016 6th International Workshop on Computer Science and Engineering, WCSE 2016

Connected dominated set based node centrality in wireless networks

This paper presents generalized spatiotemporal analysis and lookup tool (GESTALT), an unsupervised framework for content-based video retrieval. GESTALT takes a query video and retrieves “similar” videos from the database. Motion and dynamics of appearance (shape) patterns of a prominent moving foreground object are considered as the key components of the video content and captured using corresponding feature descriptors. GESTALT automatically segments the moving foreground object from the given query video shot and estimates the motion trajectory. A graph-based framework is used to explicitly capture the structural and kinematics property of the motion trajectory, while an improved version of an existing spatiotemporal feature descriptor is proposed to model the change in object shape and movement over time. A combined match cost is computed as a convex combination of the two match scores, using these two feature descriptors, which is used to rank-order the retrieved video shots. Effectiveness of GESTALT is shown using extensive experimentation, and comparative study with recent techniques exhibits its superiority. © 2015, Springer-Verlag London.

Signal, Image and Video Processing

Use of trajectory and spatiotemporal features for retrieval of videos with a prominent moving foreground object

Alarm Diffusion Systems (ADS) have complex, exacting and critical requirements. In this work we aim to provide a software architecture perspective towards ADS. We look at both functional and quality requirements for an ADS and also attempt to identify certain quality attributes specific to an ADS and attempted to provide a set of architectural tactics to realise them. We also propose a Reference Architecture for designing such systems. We have provided ample examples to support our inferences and take a deeper look at a case study of the Traffic Collision Avoidance System (TCAS) in aircrafts. © 2016 Lavoisier.

Ingenierie des Systemes d'Information

An architectural roadmap towards building an alarm diffusion system [Roadmap architecturale pour la création d’un système de diffusion d’alarme]

In multi-instance data, every object is a bag that contains multiple elements or instances. Each bag may be assigned to one or more classes, such that it has at least one instance corresponding to every assigned class. However, since the annotations are at bag-level, there is no direct association between the instances within a bag and the assigned class labels, hence making the problem significantly challenging. While existing methods have mostly focused on Bag-to-Bag or Class-to-Bag distances, in this paper, we address the multiple instance learning problem using a novel Bag-to-Class distance measure. This is based on two observations: (a) existence of outliers is natural in multi-instance data, and (b) there may exist multiple instances within a bag that belong to a particular class. In order to address these, in the proposed distance measure (a) we employ L1-distance that brings robustness against outliers, and (b) rather than considering only the most similar instance-pair during distance computation as done by existing methods, we consider a subset of instances within a bag while determining its relevance to a given class. We parameterize the proposed distance measure using class-specific distance metrics, and propose a novel metric learning framework that explicitly captures inter-class correlations within the learned metrics. Experiments on two popular datasets demonstrate the effectiveness of the proposed distance measure and metric learning. © 2016 ACM.

MM 2016 - Proceedings of the 2016 ACM Multimedia Conference

A robust distance with correlated metric learning for multi-instance multi-label data

1 Road accidents cause an estimated 1.3 million fatali- ties each year worldwide. We believe that mobile devices can play a positive role by detecting various driving related events like red light cutting, rash driving and many more. We focus on a specific problem that is responsible for many accidents in India: the stopping behaviour of buses espe- cially in the vicinity of bus stops. We propose a smartphone- based system that specifically seeks to detect and report the following scenarios. 1. Has the bus come to a complete stop(instead of a rolling stop)? 2. Has the bus stopped in the left lane? 3. Has the bus stopped exactly at the bus stop? © 2016 Copyright held by the owner/author(s).

MobiSys 2016 Companion - Companion Publication of the 14th Annual International Conference on Mobile Systems, Applications, and Services

Poster: Improving road safety through smart-sensing

Purely, data-driven large scale image classification has been achieved using various feature descriptors like SIFT, HOG etc. Major milestone in this regards is Convolutional Neural Networks (CNN) based methods which learn optimal feature descriptors as filters. Little attention has been given to the use of domain knowledge. Ontology plays an important role in learning to categorize images into abstract classes where there may not be a clear visual connect between category and image, for example identifying image mood-happy, sad and neutral. Our algorithm combines CNN and ontology priors to infer abstract patterns in Indian Monument Images. We use a transfer learning based approach in which, knowledge of domain is transferred to CNN while training (top down transfer) and inference is made using CNN prediction and ontology tree/priors (bottom up transfer). We classify images to categories like Tomb, Fort and Mosque. We demonstrate that our method improves remarkably over logistic classifier and other transfer learning approach. We conclude with a remark on possible applications of the model and note about scaling this to bigger ontology. © 2015 IEEE.

2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015

Deep transfer learning with ontology for image classification

Scene change detection, one of the fundamental and most important problem of computer vision, plays a very important role in the realization of a complete industrial vision system as well as automated video surveillance system-for automatic scene analysis, monitoring, and generation of alerts based on relevant changes in a video stream. Therefore, in addition to being accurate and robust, a successful scene change detection system must also be of very high frame rate in order to detect scene changes which goes off within a glimpse of the eye and often goes unnoticeable by the conventional frame rate cameras. Keeping the high frame rate processing as main focus, a very high frame rate real-time scene change detection system is developed by leveraging VLSI design to achieve high performance. This is accomplished by proposing, designing, and implementing an area-efficient scene change detection VLSI architecture on FPGA-based IDP Express platform. The developed prototype of complete real-time scene change detection system is capable of processing 2000 frames per second for 512 ×512 video resolution and is tested for live incoming video streams from high speed camera. The proposed and implemented system architecture is adaptable and scalable for different video resolutions and frame rates. © Springer International Publishing AG 2017.

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

High frame rate real-time scene change detection system

Multimodal data mining refers to analyzing more than one form of data for discovering hidden patterns. multimodal data analytics offer immense opportunities to integrate text, visual, audio, sensor data and structured information to derive and validate insights none of which may be possible to obtain from any single source. In this chapter, we have examined different aspects of dealing with big multimodal data. We have presented an example of cross-modal information integration between text and image. We have described an application of multi-structured data mining system for business analytics. © 2017 by World Scientific Publishing Co. Pte. Ltd.

Pattern Recognition and Big Data

Mining multimodal data

Iris recognition has become an important tool for human authentication. An efficient and robust iris recognition model based on sparse representation using compressive sensing and k-nearest subspace (segments) has been proposed; k-nearest subspace approach is used for short listing the classes to reduce the time. The shortlisted candidates are divided into sectors and the sparse recognition is applied to each sector. Three classifiers: k-nearest distance classifier, Sector based classifier and Cumulative Sparse Concentration Index (CSCI) based classifiers have been used. An additive function based classifier combination scheme has been adopted in which each classifier is associated with a weight. Genetic algorithm is used to learn the weight of each of the classifier. Results obtained on different databases show that the scheme is highly robust with FAR almost zero. © 2016 Elsevier B.V. All rights reserved.