Journal of Image Processing & Pattern Recognition progress vol 3 issiue 3

ISSN 2394-1995 (Online)

Journal of Image Processing & Pattern Recognition Progress (JoIPPRP) September–December 2016

STM JOURNALS Scientific

Technical

Medical

www.stmjournals.com

STM Journals STM Journals, a strong initiative by Consortium E-Learning Network Private Ltd. (established 2006), was launched in the year 2010 under the support and guidance by our esteemed Editorial and Advisory Board Members from renowned institutes.

Objectives: 

Promotion of Scientific, Technical and Medical research.



Publication of Original Research/Review, Short Articles and Case Studies through Peer Review process.



Publishing Special Issues on Conferences.



Preparing online platform for print journals.



Empowering the libraries with online and print Journals in Scientific, Technical and Medical domains.



Publishing and distribution of books on various subjects in the category of Nanotechnology, Scientific and Technical Writing, and Environment, Health and Safety.

Salient Features: 

A bouquet of 100+ Journals that fall under Science, Technical and Medical domains.



Employs Open Journals System (OJS)—a journal management and publishing system.



The first and one of the fastest growing publication website in India as well as in abroad for its quality and coverage.



Rapid online submission and publication of papers, soon after their formal acceptance/finalization.



Facilitates linking with the other authors or professionals.



Worldwide circulation and visibility.

Journal of Image Processing & Pattern Recognition Progress ISSN: 2394-1995 (online)

Focus and Scope Covers Image digital representation Biometrics New algorithms and/or technologies for biometrics Element of visual perception Analysis of specific applications Restoration Models: Constrained & Unconstrained Processing and analysis Journal of Image Processing & Pattern Recognition Progress is published (frequency: three times a year) in India by STM Journals (division of Consortium e-Learning Network Private Ltd. Pvt.) The views expressed in the articles do not necessarily reflect of the Publisher. The publisher does not endorse the quality or value of the advertised/sponsored products described therein. Please consult full prescribing information before issuing a prescription for any products mentioned in this publication.

No part of this publication may be reproduced, stored in retrieval system or transmitted in any from without written permission of the publisher.

To cite any of the material contained in this Journal, in English or translation, please use the full English reference at the beginning of each article. To reuse any of the material, please contact STM Journals (info@stmjournals.com)

STM Journals (division of Consortium e-Learning Network Private Ltd. ) having its Marketing office located at Office No. 4, First Floor, CSC pocket E Market, Mayur Vihar Phase II, New Delhi-110091, India is the Publisher of Journal.

Statements and opinions expressed in the Journal reflect the views of the author(s) and are not the opinion of STM Journals unless so stated.

Subscription Information and Order:  National Subscription: Print - Rs 3750/- per Journal ( includes 3 print issues), Single Issue copy purchase: Rs 1500. Online - Rs 3750/- per Journal inclusive Service Tax ( includes 3 online issues), Single Issue purchase: Rs 1500

inclusive Service Tax Print + Online - Rs 5000/- per Journal inclusive Service Tax ( includes 3 print & online issues).  International Subscription:  Online Only- $199, Print Only-$299 (includes 3 print issues)  Online + Print-$399 (includes 3 print issues + online access of published back volumes )

To purchase print compilation of back issues please send your query at info@stmjournals.com Subscription must be prepaid. Rates outside of India includes delivery. Prices subject to change without notice.

Mode of Payment: At par cheque, Demand draft, and RTGS (payment to be made in favor of Consortium E-Learning Network. Pvt. ltd., payable at Delhi/New Delhi.

Online Access Policy A). For Authors: In order to provide maximum citation and wide publicity to the authors work, STM Journals also have Open Access Policy, authors who would like to get their work open access can opt for Optional Open Access publication at nominal cost as follows India, SARC and African Countries: INR 2500 or 100 USD including single hard copy of Author's Journal.

Other Countries: USD 200 including single hard copy of Author's Journal.

B). For Subscribers:  Online access will be activated within 72 hours of receipt of the payment (working days), subject to receipt of

correct information on user details/Static IP address of the subscriber.  The access will be blocked:  If the user requests for the same and furnishes valid reasons for blocking.  Due to technical issue.  Misuse of the access rights as per the access policy.

Advertising and Commercial Reprint Inquiries: STM Journals with wide circulation and visibility offer an excellent media for showcasing/promotion of your products/services and the events-namely, Conferences, Symposia/Seminars etc. These journals have very high potential to deliver the message across the targeted audience regularly with each published issue. The advertisements on bulk subscriptions, gift subscriptions or reprint purchases for distribution etc. are also very welcome.

Lost Issue Claims: Please note the following when applying for lost or missing issues:  Claims for print copies lost will be honored only after 45 days of the dispatch date and before publication of the

next issue as per the frequency.  Tracking id for the speed post will be provided to all our subscribers and the claims for the missing Journals will

be entertained only with the proofs which will be verified at both the ends.  Claims filed due to insufficient (or no notice) of change of address will not be honored.  Change of Address of Dispatch should be intimated to STM Journals at least 2 months prior to the dispatch

schedule as per the frequency by mentioning subscriber id and the subscription id.  Refund requests will not be entertained.

Legal Disputes All the legal disputes are subjected to Delhi Jurisdiction only. If you have any questions, please contact the Publication Management Team: info@stmjournals.com; Tel : +91 0120-4781211.

PUBLICATION MANAGEMENT TEAM Chairman Mr. Puneet Mehrotra Director

Group Managing Editor Dr. Archana Mehrotra Managing Director CELNET, Delhi, India

Internal Members Gargi Asha Jha Manager (Publications)

Quaisher J Hossain Senior Editor Senior Associate Editors

Himani Pandey Isha Chandra

Meenakshi Tripathi Shivani Sharma

Associate Editors Shambhavi Mishra

Sugandha Mishra

External Members Bimlesh Lochab Assistant Professor Department of Chemistry School of Natural Sciences, Shiv Nadar University Gautam Buddha Nagar, Uttar Pradesh, India

Dr. Rajiv Prakash Professor and Coordinator School of Materials Science and Technology Indian Institute of Technology (BHU), Varanasi Uttar Pradesh, India

Prof. S. Ramaprabhu Alternative Energy and Nanotechnology Technology Laboratory, Department of Physics Indian Institute of Technology, Chennai Tamil Nadu, India

Dr. Khaiser Nikam Professor, Library and Information Science Department of Library and Information Science University of Mysore Mysore, India

Dr. Yog Raj Sood Dean (Planning and Development) Professor, Department of Electrical Engineering National Institute of Technology, Hamirpur Himachal Pradesh, India

Prof. Chris Cannings Professor, School of Mathematics and Statistics University of Sheffield, Sheffield United Kingdom

Dr. Rakesh Kumar Assistant Professor Department of Applied Chemistry BIT Mesra, Patna, Bihar, India

Dr. Durgadas Naik Associate Professor (Microbiology) Management and Science University, University Drive, Seksyen13 Selangor, Malaysia

Prof. José María Luna Ariza Department of Computer Sciences and Numerical Analysis Campus of Rabanales University of Córdoba, Spain

Dr. D. K. Vijaykumar MS, MCh (Surgical Oncology), Professor and Head Department of Surgical Oncology Amrita Institute of Medical Sciences and Research Centre Ponekkara, Cochin, Kerala, India

STM JOURNALS

ADVISORY BOARD Dr. Baldev Raj

Dr. Hardev Singh Virk

Director, National Institute of Advanced Studies Indian Institute of Science campus Bangalore Karnataka, India Former Director Indira Gandhi Centre for Atomic Research, Kalpakkam, Tamil Nadu, India

Visiting Professor, Department of Physics University of SGGS World University Fatehgarh Sahib, Punjab, India Former Director Research DAV Institute of Engineering and Technology Jallandhar, India

Dr. Bankim Chandra Ray Professor and Ex-Head of the Department Department of Metallurgical and Materials Engineering National Institute of Technology, Rourkela Odisha, India

Prof. D. N. Rao Professor and Head Department of Biochemistry All India Institute of Medical Sciences New Delhi, India

Dr. Pankaj Poddar

Dr. Nandini Chatterjee Singh

Senior Scientist Physical and Materials Chemistry Division, National Chemical Laboratory Pune, Maharastra India

Additional Professor National Brain Research Centre Manesar, Gurgaon Haryana, India

Prof. Priyavrat Thareja

Dr. Ashish Runthala

Director Principal Rayat Institute of Engineering and Information Technology Punjab, India

Lecturer, Biological Sciences Group Birla Institute of Technology and Science Pilani, Rajasthan, India

Dr. Shrikant Balkisan Dhoot

Prof. Yuwaraj Marotrao Ghugal

Senior Research Scientist, Reliance Industries Limited, Mumbai, India Former Head (Research and Development) Nurture Earth R&D Pvt Ltd., MIT Campus Beed Bypass Road, Aurangabad Maharashtra, India

Professor and Head Department of Applied Mechanics Government College of Engineering Vidyanagar, Karad Maharashtra, India

STM JOURNALS

ADVISORY BOARD Dr. Baskar Kaliyamoorthy

Dr. Shankargouda Patil

Associate Professor Department of Civil Engineering National Institute of Technology, Trichy Tiruchirappalli, Tamil Nadu, India

Assistant Professor Department of Oral Pathology KLE Society's Institute of Dental Sciences Bangalore, Karnataka, India

Prof. Subash Chandra Mishra

Prof. Sundara Ramaprabhu

Professor Department of Metallurgical and Materials Engineering National Institute of Technology, Rourkela Odisha, India

Professor Department of Physics Indian Institute of Technology Madras Chennai, Tamil Nadu India

Dr. Rakesh Kumar Assistant Professor Department of Applied Chemistry Birla Institute of Technology Patna, Bihar, India

Editorial Board

Chun Ming Chang

Prof. Hsu-Yung Cheng

Assistant Professor Department of Applied Informatics and Multimedia Asia University Wufeng, Taichung 41354, Taiwan.

Associate Professor Department of Computer Science and Information Engineering National Central University, Taiwan.

Dr. Abhishek Das Assistant Professor Dept. of Information Technology, Tripura University (A Central University) Suryamaninagar, Agratala.

Dr. V. K. Govindan Professor Computer science and engineering, and Dean Academic National Institute of Technology Calicut, Kerala, India.

Dr. Hari Om Assistant Professor Department of Computer Science & Engineering Indian School of Mines Dhanbad826 004 Jharkhand India.

Dr. U. S. Reddy Assistant Professor Department of Computer Applications National Institute of Technology Tiruchirappalli – 620 015 India.

Prof. Mu-Chun Su Professor Dept. of Computer Science and Information Engineering National Central University, Taiwan.

Dr. Rameswar Debnath Head Computer Science and Engineering Discipline.

Khulna University, Khulna 9208, Bangladesh.

Dr. Bijan Karimi Professor Electrical & Computer Engineering and Computer Science Tagliatela College of Engineering, India.

Arup Kumar Pal Assistant Professor Department of Computer Science and Engineering Indian School of Mines, Dhanbad Jharkhand-826004, India.

Prof. Dilip Singh Sisodia Assistant Professor Department of Computer Science & Engineering National Institute of Technology Raipur India.

Gonzalo Vegas Associate Teacher University of Valladolid (Spain).

Director's Desk

STM JOURNALS

It is my privilege to present the print version of the [Volume 3 Issue 3] of our Journal of Image Processing & Pattern Recognition Progress, 2016. The intension of JoIPPRP is to create an atmosphere that stimulates vision, research and growth in the area of Image Processing.

Timely publication, honest communication, comprehensive editing and trust with authors and readers have been the hallmark of our journals. STM Journals provide a platform for scholarly research articles to be published in journals of international standards. STM journals strive to publish quality paper in record time, making it a leader in service and business offerings.

The aim and scope of STM Journals is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high level learning, teaching and research in all the Scientific, Technical and Medical domains.

Finally, I express my sincere gratitude to our Editorial/ Reviewer board, Authors and publication team for their continued support and invaluable contributions and suggestions in the form of authoring writeups/reviewing and providing constructive comments for the advancement of the journals. With regards to their due continuous support and co-operation, we have been able to publish quality Research/Reviews findings for our customers base.

I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.

Dr. Archana Mehrotra Managing Director STM Journals

Journal of Image Processing & Pattern Recognition Progress

Contents

1. Bangla Handwritten Digit and Basic Letter Recognition Using Machine Learning Techniques Juthika Majumder, Razia Sultana, Rameswar Debnath

1

2. Hybrid HMM/ANN Models for Improving Offline Handwritten Text Recognition Varidhi N.K., Savitha C.K., Prajna M.R., Ujwal U.J.

16

3. A Review of Image Watermarking on Different Methods with its Applications Suniti Bhadoriya, Nirupma Tiwari

24

4. Segmentation Methods Applied on MR Medical Application Vandana Rajput, Nirupma Tiwari, Manoj Ramaiya

30

5. Ranking of Image Search by Click-Based Uniformity and Typicality Ashwath M., Balapradeep K.N., Savitha C.K., Ujwal U.J.

39

Journal of Image Processing & Pattern Recognition Progress

ISSN: 2394-1995(online) Volume 3, Issue 3 www.stmjournals.com

Bangla Handwritten Digit and Basic Letter Recognition Using Machine Learning Techniques Juthika Majumder, Razia Sultana, Rameswar Debnath* Computer Science and Engineering Discipline, Khulna University, Bangladesh Abstract

The usage of computer is increasing day by day in Bangladesh, so the use of Bangla is increasing in computer. Furthermore, the use of Bangla handwritten character is also increasing in many computer applications. There exist many techniques for recognition of handwritten character. From the studies we see that machine learning techniques are better techniques for recognition of handwritten character than others. A necessary prerequisite for measuring the performance of machine learning techniques is a large dataset for training. In this paper our contributions are two-fold. The first one is to develop large real dataset. We have developed a dataset containing 25,000 samples of Bangla handwritten digits and a dataset containing 10,200 samples of Bangla handwritten letters. Each sample in the databases is an image of size 1616 pixels. The second contribution is to a systematic evaluation of the performance of machine learning techniques for Bangla character recognition. In this paper, we choose mostly used machine learning techniques: one-againstone support vector machine, one-against-all support vector machine, artificial neural networks, and radial basis neural network. We apply several morphological operations on image dataset that can improve the performance of machine learning techniques for handwritten character recognition. From the experimental results we see that classifiers show better results with preprocessed data, and support vector machines give significantly better recognition results than those of radial basis neural network and artificial neural networks.

From the results we see that 99.10% accuracy is obtained for digit recognition and 96.08% accuracy is obtained for letter recognition by support vector machine.

Keywords: Support vector machine, artificial neural network, radial basic function network, principal component analysis, flood fill algorithm, thinning, dilation, opening, binarization

INTRODUCTION

Now-a-days, handwritten character recognition systems are used in variety of practical and commercial applications such as mail sorting, bank processing, document reading and postal address recognition, etc.

The application areas of handwritten character recognition are increasing day by day. Thus, an automatic and good recognition system is desired in every language.

Handwritten characters are nonuniform in nature. Different writers write a particular character in different styles and sizes. Even the same writer can write the same character in different styles and sizes at different times. For multiscript writers, writing style of one script has impact on other similar script. Handwritten character recognition has been one of the most challenging research areas in the field of image processing and pattern recognition. It becomes more challenging and sometimes intractable when the number of letters in

Alphabet set is large and many of them are very similar.

A lot of research has been done in handwritten character recognition and numerous techniques have been proposed. These include template matching techniques, statistical classifiers such as Bayes’ classifier, Hidden Markov Model (HMM), graph-based and automata based synaptic classifiers, and machine learning based techniques [1–13].

Among these techniques, the approaches based on HMM and support vector machine (SVM) (a machine learning technique) are popular in handwritten recognition tasks due to their high recognition rate.

JoIPPRP (2016) 1-15 © STM Journals 2016. All Rights Reserved

Page 1

Journal of Image Processing & Pattern Recognition Progress

ISSN: 2394-1995(online) Volume 3, Issue 3 www.stmjournals.com

Hybrid HMM/ANN Models for Improving Offline Handwritten Text Recognition Varidhi N.K., Savitha C.K., Prajna M.R., Ujwal U.J.

Department of Computer science, KVG College of Engineering, Sullia, Karnataka, India

Abstract

In this approach, it proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing the unconstrained offline handwritten texts.

Handwritten image normalization from a scanned image includes several steps, usually it begin with image cleaning, page skew correction, and line detection. For handwritten text line image several pre-processing steps to reduce variation in writing style are performed like slope and slant removal and character size normalization. The structural part has been modeled with Markov chains, and a Multilayer Perceptron (MLP) is used to estimate the emission probabilities. This approach presents a new technique to remove slope and slant from the handwritten text and also to normalize the size of text images with supervised learning methods. Slope correction and size normalizations are achieved by classifying the local extrema of text contours with MLP. With the help of Artificial Neural Network slant is removed in a nonuniform way. Experiments have been conducted on many offline handwritten text lines and the recognition rates are achieved.

Keywords: Handwriting recognition, offline handwriting, HMM, hybrid HMM/ANN, neural networks, image normalization, multilayer perceptron

INTRODUCTION

Handwriting recognition has been one of the most popular and challenging research areas in the field of image processing and pattern recognition. The field of handwriting recognition can be divided into two types, one is on-line and another one is off-line recognition. In on-line recognition the writer is connected physically to a computer via a mouse, or any touch sensitive device and his or her handwriting is recorded as a time dependent process. But in off-line mode, handwritten text is captured by means of a scanner and becomes available as image without any temporal information. And also the use of cameras for capturing handwriting is becoming increasingly popular today.

Off-line recognition is considered the more difficult problem because of the lack of temporal information [1]. Offline handwritten text recognition is the most active areas of research in computer science. It is very difficult to recognize the handwritten text because of the high variability of writing styles [2]. The off-line and on-line handwritten character recognition techniques have different approaches, and they share a lot of common problems and solutions. Nowadays, in addition

to the powerful computers and more accurate electronic equipment such as cameras, scanners, and other electronic tablets, it uses modern methodologies such as Neural Networks (NNs), Hidden Markov Models (HMMs) and many more.

LITERATURE SURVEY

Machine simulation of human functions is a challenging research field as a result of the advent of digital computers. In the areas where it requires certain amount of intelligence, such as number crunching or chess playing, improvements are achieved. Humans still perform even the most powerful computers in relatively routine functions such as vision.

Machine simulation of human reading is an intensive research area.

In this overview, character recognition is used as an umbrella word, which covers all types of machine recognition of characters in various application domains [2].

JoIPPRP (2016) 16-23 Š STM Journals 2016. All Rights Reserved

Page 16

Journal of Image Processing & Pattern Recognition Progress

ISSN: 2394-1995(online) Volume 3, Issue 3 www.stmjournals.com

A Review of Image Watermarking on Different Methods with its Applications Suniti Bhadoriya*, Nirupma Tiwari Department of Computer Science and Engineering, Shri Ram College of Engineering and Management, Banmore, Madhya Pradesh, India Abstract

Watermarking is a process to hide some secret data in a cover file. Watermarking is a notion intently related to steganography. In these, both hide information in an image. The information hidden in this is in different forms like an image, song, video within the signal itself. We have studied comparative analysis of various approaches that may positive and negative of these techniques. This comparison can further be used to improvise and propose few new techniques for the same.

Keywords: Applications, Techniques, Watermarking

INTRODUCTION

Present day generation is witness of digital media improvements. Using phone camera to capture a photo is a simple example. The use of digital media is common in present era.

Text, audio, video are other digital media example.

 

Applications and classification of watermarking Image watermarking threats.

A watermark image is shown in Figure 1.

We know an internet is the fastest medium of transferring data to any place in a world. This method can be using to every digital media types such as image, audio, video and documents. From many years researchers and developers worked in this area to gain best results [1].

These are few sections for basic information purpose:  Overview and history of Image watermarking  Various types of Image watermarking techniques

Fig. 1: A Watermark Image.

BASIC OF WATERMARKING

Digital Image Watermarking contains of two parts: 1. Watermark embedding 2. Watermark extraction

JoIPPRP (2016) 24-29 © STM Journals 2016. All Rights Reserved

Page 24

Journal of Image Processing & Pattern Recognition Progress

ISSN: 2394-1995(online) Volume 3, Issue 3 www.stmjournals.com

Segmentation Methods Applied on MR Medical Application Vandana Rajput*, Nirupma Tiwari, Manoj Ramaiya Department of Computer Science and Engineering, ShriRam College of Engineering and Management, Gwalior, Madhya Pradesh, India Abstract

Image segmentation techniques as applied in MRI of human brain are developing by leaps and bounds. The human brain is the most imaging design of the nature. Cancer and neurological problems are resolved using MR of the brain. The automatic diagnostic system starts with medical imaging, segmentation and analysis of the findings and then the final decisions which are further used to treat the patient. In the real world certain noises are present. Segmentation is the process of separation of region of interest from the main image.

It is a challenging task to de noise the image. Large member of algorithms for segmentation have been in practice. This review paper briefs the research made and published in the journals of international repute. The review is presented in the chronological order. The focus is on MR image segmentation and their result.

Keywords: Brain, MRI, Segmentation, Robust Fuzzy C-means clustering (RFCM), Image segmentation, Nonlocal, Brain tissue

INTRODUCTION

Human beings and their brain has always been the focus of the medical research. The image processing is the fast growing area dedicated to medical imaging. Magnetic resonance (MR) brain images are analyzed and ROI is segmented and further compared with data collected during machine learning. There are many problems of anatomy and neuro anatomical valuation in the normal human brains, the segmentation of brain tumor from MRI is an important process for treatment, planning, monitoring of therapy, examining effect of radiation, drug treatments and researching and making distinction between healthy brain and brain having tumor. MRI input parameters can be modified to get the image with clear contrast. The different gray levels in the output display the different cases of neuropathology. Neuroscience uses mapping of functional activation onto brain anatomy, the study of brain development and the analysis of neuroanatomical variability in normal brains requires the identification of brain structures in MRI images.

IMAGE SEGMENTATION USING FUZZY C-MEAN ALGORITHM TECHNIQUE

Yuan et al. have used soft method to find the region of interest [1]. He uses Fuzzy C-means algorithm. In this method Fuzzy membership of each class is calculated to find out the segmentation of entire image. Fuzzy C-means (FCM) algorithm uses recursive method to achieve the above objective. It uses only the gray information of a pixel without considering the relationship with other pixels.

Thus this membership determination is vulnerable to noise. In order to achieve better results the membership function is post processed. This increases the spatial constraint to get robustness against noise. The author has further discussed RFCM (Robust FCM) which gives better result as compared with FCM. In this work robust Fuzzy C-Means clustering is enhanced by appending nonlocal component.

The authors have simulated the MR brain images and proved that the nonlocal Fuzzy CMeans clustering yields better for noisy images.

JoIPPRP (2016) 30-38 Š STM Journals 2016. All Rights Reserved

Page 30

Journal of Image Processing & Pattern Recognition Progress

ISSN: 2394-1995(online) Volume 3, Issue 3 www.stmjournals.com

Ranking of Image Search by Click-Based Uniformity and Typicality Ashwath M*., Balapradeep K.N., Savitha C.K., Ujwal U.J.

Department of Computer Science, KVG College of Engineering, Sullia, Karnataka, India

Abstract

According to the image search re-ranking, apart from the semantic gap and intent gap, which is the gap between the representation of user’s query and the real objective of the users, is forming a major issue in restricting the evolution of image retrieval. In order to reduce human effects, the method use image click-through data, which can be viewed as the implicative feedback from users, to help overcome the intention gap, and further to improve the performance of image search. The proposed method presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality.

Keywords: Image search, search re-ranking, click-through data, multifeature similarity, image typicality

INTRODUCTION

Hundreds of thousands of images are uploaded to the internet with the help of online social media and by the popularity of mobile devices thus, building a satisfying image retrieval system is the challenge to improve the user search experience [1]. In order to improve search performance, image search re-ranking, which adjusts the initial ranking orders by mining visual content or leveraging some auxiliary knowledge, is proposed, and has been the focus of attention in both academia and industry in recent years [2, 3].

Most of the existing re-ranking methods utilize the visual information in an unsupervised and the passive manner to overcome the “semantic gap” (the gap between the low-level features and high-level of semantics) [4, 5]. Although multiple visual methods have been used to further mine useful visual information, they can only establish limited performance improvements [6, 7]. The contributions of this method can be summarized as follows:  Present a novel image search re-ranking, named spectral clustering re-ranking with click-based similarity and typicality (SCCST), which first use image click data to guide image similarity learning for multiple features, then conducts spectral clustering to group similar images into clusters, and obtained the re-ranking results by calculating click-based clusters





typicality and within the clusters click based image typicality in descending order [8].

Use click-through data and multiple visual modalities simultaneously to learn image similarity, and propose an innovative similarity learning algorithm, called click based multifeature similarity learning (CMSL), which conducts metric learning based on click-based triplets selection, while integrating multi-feature into a unified similarity space via multiple kernel learning [9].

Integrate click-through data with image typicality learning to mine the influence of this implicit feedback in determining the degree of image relevance to the given query, and further improve the image search performance [9].

RELATED WORK Visual Search Re-Ranking (2010) According to the approach it proposed a novel visual re-ranking method by mining relevant visual patterns from the search results which are available from existing search engines on the Internet. There are several open problems.

First, the number of search engines or online resources is still limited. It would be a promising topic to discover more search engines and sites and investigate how many

JoIPPRP (2016) 39-46 © STM Journals 2016. All Rights Reserved

Page 39

ISSN 2394-1995 (Online)

Journal of Image Processing & Pattern Recognition Progress (JoIPPRP) September–December 2016

STM JOURNALS Scientific

Technical

Medical

www.stmjournals.com