Communicationsin Computer and Information Science 1148

Commenced Publication in 2007Founding and Former Series Editors:Phoebe Chen, Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu,Krishna M. Sivalingam, Dominik Ślęzak, Takashi Washio, Xiaokang Yang,and Junsong Yuan

Editorial Board Members

Simone Diniz Junqueira BarbosaPontifical Catholic University of Rio de Janeiro (PUC-Rio),Rio de Janeiro, Brazil

Joaquim FilipePolytechnic Institute of Setúbal, Setúbal, Portugal

Ashish GhoshIndian Statistical Institute, Kolkata, India

Igor KotenkoSt. Petersburg Institute for Informatics and Automation of the RussianAcademy of Sciences, St. Petersburg, Russia

Lizhu ZhouTsinghua University, Beijing, China

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Neeta Nain • Santosh Kumar Vipparthi •

Balasubramanian Raman (Eds.)

Computer Vision andImage Processing4th International Conference, CVIP 2019Jaipur, India, September 27–29, 2019Revised Selected Papers, Part II

123

EditorsNeeta NainMalaviya National Institute of TechnologyJaipur, Rajasthan, India

Santosh Kumar VipparthiMalaviya National Institute of TechnologyJaipur, Rajasthan, India

Balasubramanian RamanIndian Institute of Technology RoorkeeRoorkee, Uttarakhand, India

ISSN 1865-0929 ISSN 1865-0937 (electronic)Communications in Computer and Information ScienceISBN 978-981-15-4017-2 ISBN 978-981-15-4018-9 (eBook)https://doi.org/10.1007/978-981-15-4018-9

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Preface

This volume contains the papers from the 4th International Conference on ComputerVision and Image Processing (CVIP 2019). The event was endorsed by the Interna-tional Association for Pattern Recognition (IAPR) and organized by Malaviya NationalInstitute of Technology, Jaipur, during September 27–29, 2019.

CVIP is a premier conference focused on image, video processing, and computervision. The conference featured world-renowned speakers, technical workshops, anddemonstrations.

CVIP 2019 acted as a major forum for presentation of technological progress andresearch outcomes in the area of image processing and computer vision, serving as aplatform for exchange between academia and industry. The selected papers come fromaround 202 original submissions by researchers based in several countries includingSouth Korea, Norway, Malaysia, Iceland, Ethiopia, Canada, Bangladesh, India, and theUSA. The highly diversified audience gave us the opportunity to achieve a good levelof understanding of the mutual needs, requirements, and technical means available inthis field of research.

The topics included in this edition of CVIP the following fields connected tocomputer vision and image processing: data acquisition and modeling, visualizationand audio methods, sensors and actuators, data mining, image enhancement andrestoration, segmentation, object detection and tracking, video analysis and summa-rization, biometrics and forensics, deep learning, document image analysis, remotesensing, multi-spectral and hyper-spectral image processing, etc. All the acceptedpapers were double-blind peer reviewed by three qualified reviewers chosen from ourTechnical Committee based on their qualifications, areas of interest, and experience.The papers were evaluated on their relevance to CVIP 2019 tracks and topics, scientificcorrectness, and clarity of presentation. Selection was based on these reviews and onfurther recommendations by the Program Committee.

The editors of the current proceedings are very grateful and wish to thank thededicated Technical Committee members and all the other reviewers for their valuablecontributions, commitment, and enthusiastic support. We also thank CCIS at Springerfor their trust and for publishing the proceedings of CVIP 2019.

September 2019 Neeta NainSantosh Kumar VipparthiBalasubramanian Raman

Organization

Organizing Committee

Neeta Nain MNIT Jaipur, IndiaSantosh Kumar Vipparthi MNIT Jaipur, IndiaPartha Pratim Roy IIT Roorkee, IndiaAnanda Shankar

ChowdharyJadavpur University, India

Program Committee

Balasubramanian Raman IIT Roorkee, IndiaSanjeev Kumar IIT Roorkee, IndiaArnav Bhaskar IIT Mandi, IndiaSubramanyam Murala IIT Ropar, IndiaAbhinav Dhall IIT Ropar, India

International Advisory Committee

Uday Kumar R. Yaragatti MNIT Jaipur, IndiaAnil K. Jain Michigan State University, USABidyut Baran Chaudhari ISI Kolkata, IndiaMohamed Abdel Mottaleb University of Miami, USAMohan S. Kankanhalli NUS, SingaporeAjay Kumar Hong Kong Poly University, Hong KongAles Prochazka Czech Technical University, Czech RepublicAndrea Kutics ICU, JapanDaniel P. Lopresti Lehigh University, USAGian Luca Foresti University of Udine, ItalyJonathan Wu University of Windsor, CanadaJosep Llados University of Barcelona, SpainKokou Yetongnon University of Burgundy, FranceKoichi Kise Osaka Prefecture University, JapanLuigi Gallo National Research Council, ItalySlobodan Ribaric University of Zagreb, CroatiaUmapada Pal ISI Kolkata, IndiaXiaoyi Jiang University of Münster, Germany

Local Committee

Emmanuel S. Pilli MNIT Jaipur, IndiaDinesh Kumar Tyagi MNIT Jaipur, IndiaVijay Laxmi MNIT Jaipur, IndiaArka Prakash Mazumdar MNIT Jaipur, IndiaMushtaq Ahmed MNIT Jaipur, IndiaYogesh Kumar Meena MNIT Jaipur, IndiaSatyendra Singh Chouhan MNIT Jaipur, IndiaMahipal Jadeja MNIT Jaipur, IndiaMadhu Agarwal MNIT Jaipur, IndiaKuldeep Kumar MNIT Jaipur, IndiaPrakash Choudhary NIT Hamirpur, IndiaMaroti Deshmukh NIT Uttarakhand, IndiaSubhash Panwar GEC Bikaner, IndiaTapas Badal Bennett University, IndiaSonu Lamba MNIT Jaipur, IndiaRiti Kushwaha MNIT Jaipur, IndiaPraveen Kumar Chandaliya MNIT Jaipur, IndiaRahul Palliwal MNIT Jaipur, IndiaKapil Mangal MNIT Jaipur, IndiaRavindra Kumar Soni MNIT Jaipur, IndiaGopal Behera MNIT Jaipur, IndiaSushil Kumar MNIT Jaipur, India

Sponsors

viii Organization

Contents – Part II

Neural Network

Denoising Images with Varying Noises Using Autoencoders . . . . . . . . . . . . 3Snigdha Agarwal, Ayushi Agarwal, and Maroti Deshmukh

Image Aesthetics Assessment Using Multi Channel ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Nishi Doshi, Gitam Shikkenawis, and Suman K. Mitra

Profession Identification Using Handwritten Text Images . . . . . . . . . . . . . . . 25Parveen Kumar, Manu Gupta, Mayank Gupta, and Ambalika Sharma

A Study on Deep Learning for Breast Cancer Detectionin Histopathological Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Oinam Vivek Singh, Prakash Choudhary, and Khelchandra Thongam

Face Presentation Attack Detection Using Multi-classifier Fusionof Off-the-Shelf Deep Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Raghavendra Ramachandra, Jag Mohan Singh, Sushma Venkatesh,Kiran Raja, and Christoph Busch

Vision-Based Malware Detection and Classification Using LightweightDeep Learning Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

S. Abijah Roseline, G. Hari, S. Geetha, and R. Krishnamurthy

A Deep Neural Network Classifier Based on Belief Theory . . . . . . . . . . . . . 74Minny George and Praveen Sankaran

Real-Time Driver Drowsiness Detection Using Deep Learningand Heterogeneous Computing on Embedded System . . . . . . . . . . . . . . . . . 86

Shivam Khare, Sandeep Palakkal, T. V. Hari Krishnan, Chanwon Seo,Yehoon Kim, Sojung Yun, and Sankaranarayanan Parameswaran

A Comparative Analysis for Various Stroke Prediction Techniques . . . . . . . . 98M. Sheetal Singh, Prakash Choudhary, and Khelchandra Thongam

A Convolutional Fuzzy Min-Max Neural Networkfor Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Trupti R. Chavan and Abhijeet V. Nandedkar

Anomalous Event Detection and Localization Using Stacked Autoencoder . . . 117Suprit D. Bansod and Abhijeet V. Nandedkar

Kernel Variants of Extended Locality Preserving Projection . . . . . . . . . . . . . 130Pranjal Bhatt, Sujata, and Suman K. Mitra

DNN Based Adaptive Video Streaming Using Combinationof Supervised Learning and Reinforcement Learning . . . . . . . . . . . . . . . . . . 143

Karan Rakesh, Luckraj Shrawan Kumar, Rishabh Mittar,Prasenjit Chakraborty, P. A. Ankush, and Sai Krishna Gairuboina

A Deep Convolutional Neural Network Based Approach to Extractand Apply Photographic Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Mrinmoy Sen and Prasenjit Chakraborty

Video Based Deception Detection Using Deep Recurrent ConvolutionalNeural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

Sushma Venkatesh, Raghavendra Ramachandra, and Patrick Bours

Deep Demosaicing Using ResNet-Bottleneck Architecture . . . . . . . . . . . . . . 170Divakar Verma, Manish Kumar, and Srinivas Eregala

Psychological Stress Detection Using Deep ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

Kaushik Sardeshpande and Vijaya R. Thool

Video Colorization Using CNNs and Keyframes Extraction:An Application in Saving Bandwidth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

Ankur Singh, Anurag Chanani, and Harish Karnick

Image Compression for Constrained Aerial Platforms: A UnifiedFramework of Laplacian and cGAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

A. G. J. Faheema, A. Lakshmi, and Sreedevi Priyanka

Multi-frame and Multi-scale Conditional Generative Adversarial Networksfor Efficient Foreground Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

Himansu Didwania, Subhankar Ghatak, and Suvendu Rup

Ink Analysis Using CNN-Based Transfer Learning to DetectAlteration in Handwritten Words. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Prabhat Dansena, Rahul Pramanik, Soumen Bag, and Rajarshi Pal

Ensemble Methods on Weak Classifiers for Improved DriverDistraction Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

A. Swetha, Megha Sharma, Sai Venkatesh Sunkara,Varsha J. Kattampally, V. M. Muralikrishna, and Praveen Sankaran

DeepRNNetSeg: Deep Residual Neural Network for Nuclei Segmentationon Breast Cancer Histopathological Images . . . . . . . . . . . . . . . . . . . . . . . . 243

Mahesh Gour, Sweta Jain, and Raghav Agrawal

x Contents – Part II

Classification of Breast Tissue Density . . . . . . . . . . . . . . . . . . . . . . . . . . . 254Kanchan Lata Kashyap, Manish Kumar Bajpai, and Pritee Khanna

Extreme Weather Prediction Using 2-Phase Deep Learning Pipeline . . . . . . . 266Vidhey Oza, Yash Thesia, Dhananjay Rasalia, Priyank Thakkar,Nitant Dube, and Sanjay Garg

Deep Hybrid Neural Networks for Facial Expression Classification . . . . . . . . 283Aakash Babasaheb Jadhav, Sairaj Laxman Burewar,Ajay Ashokrao Waghumbare, and Anil Balaji Gonde

SCDAE: Ethnicity and Gender Alteration on CLF and UTKFace Dataset . . . . 294Praveen Kumar Chandaliya, Vardhman Kumar, Mayank Harjani,and Neeta Nain

Manipuri Handwritten Character Recognition by ConvolutionalNeural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307

Sanasam Inunganbi, Prakash Choudhary, and Khumanthem Manglem

Design and Implementation of Human Safeguard MeasureUsing Separable Convolutional Neural Network Approach . . . . . . . . . . . . . . 319

R. Vaitheeshwari, V. Sathiesh Kumar, and S. Anubha Pearline

Tackling Multiple Visual Artifacts: Blind Image RestorationUsing Conditional Adversarial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 331

M. Anand, A. Ashwin Natraj, V. Jeya Maria Jose, K. Subramanian,Priyanka Bhardwaj, R. Pandeeswari, and S. Deivalakshmi

Two-Stream CNN Architecture for Anomalous Event Detectionin Real World Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

Snehashis Majhi, Ratnakar Dash, and Pankaj Kumar Sa

3D CNN with Localized Residual Connections for HyperspectralImage Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354

Shivangi Dwivedi, Murari Mandal, Shekhar Yadav,and Santosh Kumar Vipparthi

A Novel Approach for False Positive Reductionin Breast Cancer Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364

Mayuresh Shingan, Meenakshi Pawar, and S. Talbar

Classification of Effusion and Cartilage Erosion Affects in OsteoarthritisKnee MRI Images Using Deep Learning Model . . . . . . . . . . . . . . . . . . . . . 373

Pankaj Pratap Singh, Shitala Prasad, Anil Kumar Chaudhary,Chandan Kumar Patel, and Manisha Debnath

Contents – Part II xi

Object Detection

A High Precision and High Recall Face Detectorfor Equi-Rectangular Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387

Ankit Dhiman and Praveen Agrawal

Real-Time Ear Landmark Detection Using Ensemble of Regression Trees . . . 398Hitesh Gupta, Srishti Goel, Riya Sharma,and Raghavendra Kalose Mathsyendranath

Object Recognition

A New Hybrid Architecture for Real-Time Detectionof Emergency Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413

Eshwar Prithvi Jonnadula and Pabitra Mohan Khilar

Speed Prediction of Fast Approaching Vehicle Using Moving Camera. . . . . . 423Hutesh Kumar Gauttam and Ramesh Kumar Mohapatra

Improved Performance of Visual Concept Detection in ImagesUsing Bagging Approach with Support Vector Machines . . . . . . . . . . . . . . . 432

Sanjay M. Patil and Kishor K. Bhoyar

FaceID: Verification of Face in Selfie and ID Document . . . . . . . . . . . . . . . 443Rahul Paliwal, Shalini Yadav, and Neeta Nain

Online Handwriting Recognition

A Benchmark Dataset of Online Handwritten Gurmukhi Script Wordsand Numerals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

Harjeet Singh, R. K. Sharma, Rajesh Kumar, Karun Verma,Ravinder Kumar, and Munish Kumar

Optical Character Recognition

Targeted Optical Character Recognition: ClassificationUsing Capsule Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469

Pratik Prajapati, Shaival Thakkar, and Ketul Shah

Security and Privacy

An Edge-Based Image Steganography Method Using Modulus-3 Strategyand Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485

Santosh Kumar Tripathy and Rajeev Srivastava

xii Contents – Part II

Multi-level Threat Analysis in Anomalous Crowd Videos . . . . . . . . . . . . . . 495Arindam Sikdar and Ananda S. Chowdhury

Unsupervised Clustering

Discovering Cricket Stroke Classes in Trimmed Telecast Videos. . . . . . . . . . 509Arpan Gupta, Ashish Karel, and M. Sakthi Balan

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521

Contents – Part II xiii

Contents – Part I

Biometrics

Towards Ocular Recognition Through Local Image Descriptors . . . . . . . . . . 3Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran,and Pawan Dubey

Computer Forensic

A Fast and Rigid Copy Move Forgery Detection TechniqueUsing HDBSCAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Shraddha Wankhade, Anuja Dixit, and Soumen Bag

Computer Vision

Automated Industrial Quality Control of Pipe StacksUsing Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Sayantan Chatterjee, Bidyut B. Chaudhuri, and Gora C. Nandi

Asymmetric Wide Tele Camera Fusion for High Fidelity Digital Zoom . . . . . 39Sai Kumar Reddy Manne, B. H. Pawan Prasad, and K. S. Green Rosh

Energy Based Convex Set Hyperspectral EndmemberExtraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Dharambhai Shah and Tanish Zaveri

Fast Semantic Feature Extraction Using Superpixelsfor Soft Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Shashikant Verma, Rajendra Nagar, and Shanmuganathan Raman

Spatially Variant Laplacian Pyramids for Multi-frame Exposure Fusion . . . . . 73Anmol Biswas, K. S. Green Rosh, and Sachin Deepak Lomte

Traffic Sign Recognition Using Color and Spatial Transformer Networkon GPU Embedded Development Board . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Bhaumik Vaidya and Chirag Paunwala

Unsupervised Single-View Depth Estimation for Real Time Inference . . . . . . 94Mohammed Arshad Siddiqui, Arpit Jain, Neha Gour, and Pritee Khanna

Dimension Reduction

A Novel Information Theoretic Cost Measure for Filtering BasedFeature Selection from Hyperspectral Images . . . . . . . . . . . . . . . . . . . . . . . 109

Vikas Kookna, Ankit Kumar Singh, Agastya Raj, and Biplab Banerjee

Healthcare Information Systems

CNN and RF Based Classification of Brain Tumorsin MR Neurological Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Vishlavath Saraswathi, Ankush D. Jamthikar, and Deep Gupta

Tensor Based Dictionary Learning for Compressive SensingMRI Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Minha Mubarak, Thomas James Thomas, J. Sheeba Rani,and Deepak Mishra

Nonparametric Vibration Based Damage Detection Technique forStructural Health Monitoring Using 1D CNN . . . . . . . . . . . . . . . . . . . . . . . 146

Yash Sarawgi, Shivam Somani, Ayushmaan Chhabra, and Dhiraj

Neural Network and SVM Based Kidney Stone Based MedicalImage Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Priyanka Chak, Payal Navadiya, Bhavya Parikh, and Ketki C. Pathak

Automatic Report Generation for Chest X-Ray Images:A Multilevel Multi-attention Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

Gaurav O. Gajbhiye, Abhijeet V. Nandedkar, and Ibrahima Faye

Image Processing

Medical Image Denoising Using Spline Based Fuzzy WaveletShrink Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

Pranaba K. Mishro, Sanjay Agrawal, and Rutuparna Panda

MBC-CA: Multithreshold Binary Conversion Based Salt-and-PepperNoise Removal Using Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . 195

Parveen Kumar, Mohd Haroon Ansari, and Ambalika Sharma

Image to CAD: Feature Extraction and Translation of Raster Imageof CAD Drawing to DXF CAD Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Aditya Intwala

Non-uniform Deblurring from Blurry/Noisy Image Pairs . . . . . . . . . . . . . . . 216P. L. Deepa and C. V. Jiji

xvi Contents – Part I

An Effective Video Bootleg Detection Algorithm Based on NoiseAnalysis in Frequency Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

Preeti Mehta, Sushila Maheshkar, and Vikas Maheshkar

A Novel Approach for Non Uniformity Correctionin IR Focal Plane Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Nikhil Kumar, Meenakshi Massey, and Neeta Kandpal

Calibration of Depth Map Using a Novel Target . . . . . . . . . . . . . . . . . . . . . 248Sandip Paul, Deepak Mishra, and M. Senthil

Image Segmentation

Optical Flow Based Background Subtraction Method for LungNodule Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

R. Jenkin Suji, Sarita Singh Bhadouria, Joydip Dhar,and W. Wilfred Godfrey

A Method to Generate Synthetically Warped Document Image . . . . . . . . . . . 270Arpan Garai, Samit Biswas, Sekhar Mandal, and Bidyut B. Chaudhuri

Delaunay Triangulation Based Thinning Algorithm for Alphabet Images . . . . 281Philumon Joseph, Binsu C. Kovoor, and Job Thomas

A Reduced Graph Cut Approach to Interactive Object Segmentationwith Flexible User Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291

Priyambada Subudhi, Bhanu Pratap Prabhakar,and Susanta Mukhopadhyay

A New Fuzzy Clustering Algorithm by Incorporating ConstrainedClass Uncertainty-Based Entropy for Brain MR Image Segmentation. . . . . . . 301

Nabanita Mahata and Jamuna Kanta Sing

A Novel Saliency-Based Cascaded Approach for MovingObject Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

Prashant W. Patil, Akshay Dudhane, Subrahmanyam Murala,and Anil B. Gonde

A Novel Graph Theoretic Image Segmentation Technique . . . . . . . . . . . . . . 323Sushmita Chandel and Gaurav Bhatnagar

Extraction and Recognition of Numerals from Machine-PrintedUrdu Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334

Harmohan Sharma, Dharam Veer Sharma, G. S. Lehal,and Ankur Rana

Contents – Part I xvii

Colour Sensitive Image Segmentation Using Quaternion Algebra . . . . . . . . . 348Sandip Kumar Maity and Prabir Biswas

Information Retrieval

Multimodal Query Based Approach for Document Image Retrieval . . . . . . . . 361Amit V. Nandedkar and Abhijeet V. Nandedkar

Transformed Directional Tri Concomitant Triplet Patternsfor Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372

Chesti Altaff Hussain, D. Venkata Rao, and S. Aruna Mastani

Encoder Decoder Based Image Semantic Space Creationfor Clothing Items Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383

Keshav Kumar Kedia, Gaurav Kumar Jain, and Vipul Grover

Feature Learning for Effective Content-Based Image Retrieval . . . . . . . . . . . 395Snehal Marab and Meenakshi Pawar

Instance Based Learning

Two Efficient Image Bag Generators for Multi-instanceMulti-label Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

P. K. Bhagat, Prakash Choudhary, and Kh Manglem Singh

Machine Learning

A Comparative Study of Big Mart Sales Prediction . . . . . . . . . . . . . . . . . . . 421Gopal Behera and Neeta Nain

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433

xviii Contents – Part I