Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
Color and texture based image retrievaleSAT Journals
Abstract Content-based image retrieval (CBIR) is an vital research area for manipulating bulky image databases and records. Alongside the conventional method where the images are searched on the basis of words, CBIR system uses visual contents to retrieve the images. In content based image retrieval systems texture and color features have been the primal descriptors. We use HSV color information and mean of the image as texture information. The performance of proposed scheme is calculated on the basis of precision, recall and accuracy. As an effect, the blend of color and texture features of the image provides strong feature set for image retrieval. Keywords: image retrieval, HSV color space, color histogram, image texture.
In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a
relevant image from an outsized database. Textile images showed the way for the development of CBIR. It
establishes the efficient combination of color, shape and texture features. Here the textile image is given as
dataset. The images in database are loaded. The resultant image is given as input to feature extraction
technique which is transformation of input image into a set of features such as color, texture and shape.
The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color
feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel
technique. These algorithms are used to calculate the similarity between extracted features. These features
are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification
techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by
splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large
database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix
laboratory) which is built software applications
Color and texture based image retrievaleSAT Journals
Abstract Content-based image retrieval (CBIR) is an vital research area for manipulating bulky image databases and records. Alongside the conventional method where the images are searched on the basis of words, CBIR system uses visual contents to retrieve the images. In content based image retrieval systems texture and color features have been the primal descriptors. We use HSV color information and mean of the image as texture information. The performance of proposed scheme is calculated on the basis of precision, recall and accuracy. As an effect, the blend of color and texture features of the image provides strong feature set for image retrieval. Keywords: image retrieval, HSV color space, color histogram, image texture.
Content-Based Image Retrieval (CBIR) systems employ color as primary feature with texture and shape as secondary features. In this project a simple, image retrieval system will be implemented
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Under Image processing techniques, it describes how we can extract the important part of the image and how can we compare it with the existing technologies. It also describe the future scope of this method
Multimedia content based retrieval slideshare.pptgovintech1
information retrieval for text and multimedia content has become an important research area.
Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation
from pixel values. In this presentation, an overview of the content based retrieval is presented along with
the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques
used and learning methods employed are also analyzed.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
Content-Based Image Retrieval (CBIR) systems employ color as primary feature with texture and shape as secondary features. In this project a simple, image retrieval system will be implemented
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Under Image processing techniques, it describes how we can extract the important part of the image and how can we compare it with the existing technologies. It also describe the future scope of this method
Multimedia content based retrieval slideshare.pptgovintech1
information retrieval for text and multimedia content has become an important research area.
Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation
from pixel values. In this presentation, an overview of the content based retrieval is presented along with
the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques
used and learning methods employed are also analyzed.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Low level features for image retrieval basedcaijjournal
In this paper, we present a novel approach for image retrieval based on extraction of low level features
using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of
Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair
of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns (LBP)
descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore,
DBC captures more spatial information than LBP and its variants, also it can extract more edge
information than LBP. Hence, we employ DBC technique in order to extract grey level texture features
(texture map) from each RGB channels individually and computed texture maps are further combined
which represents colour texture features (colour texture map) of an image. Then, we decomposed the
extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the
shape and local features of wavelet transformed images using Histogram of Oriented Gradients (HOG) for
content based image retrieval. The performance of proposed method is compared with existing methods on
two databases such as Wang’s corel image and Caltech 256. The evaluation results show that our
approach outperforms the existing methods for image retrieval.
Image retrieval is the major innovations in the development of images. Mining of images is used to mine latest information from
the general collection of images. CBIR is the latest method in which our target images is to be extracted on the basis of specific features of
the specified image. The image can be retrieved in fast if it is clustered in an accurate and structured manner. In this paper, we have the
combined the theories of CBIR and analysis of features of CBIR systems.
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal 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 the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic re- lationship with the query given by the user Content Based Image Retrieval Project.
http://takeoffprojects.com/content-based-image-retrieval-project
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Applications of spatial features in cbir a surveycsandit
With advances in the computer technology and the World Wide Web there has been an
explosion in the amount and complexity of multimedia data that are generated, stored,
transmitted, analyzed, and accessed. In order to extract useful information from this huge
amount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties
such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in
designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy
and simple to derive and effective. Researchers are moving towards finding spatial features and
the scope of implementing these features in to the image retrieval framework for reducing the
semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems.
Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
APPLICATIONS OF SPATIAL FEATURES IN CBIR : A SURVEYcscpconf
With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored,transmitted, analyzed, and accessed. In order to extract useful information from this hugeamount of data, many content based image retrieval (CBIR) systems have been developed in the
last decade. A typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the
database with similar features. Retrieval efficiency and accuracy are the important issues in designing Content Based Image Retrieval System. The Shape and Spatial features are quiet easy and simple to derive and effective. Researchers are moving towards finding spatial features and the scope of implementing these features in to the image retrieval framework for reducing the semantic gap. This Survey paper focuses on the detailed review of different methods and their
evaluation techniques used in the recent works based on spatial features in CBIR systems. Finally, several recommendations for future research directions have been suggested based on
the recent technologies.
Performance Evaluation Of Ontology And Fuzzybase Cbiracijjournal
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
PERFORMANCE EVALUATION OF ONTOLOGY AND FUZZYBASE CBIRacijjournal
IN THIS PAPER, WE HAVE DONE PERFORMANCE EVALUATION OF ONTOLOGY USING LOW-LEVEL FEATURES LIKE
COLOR, TEXTURE AND SHAPE BASED CBIR, WITH TOPIC SPECIFIC CBIR.THE RESULTING ONTOLOGY CAN BE USED
TO EXTRACT THE APPROPRIATE IMAGES FROM THE IMAGE DATABASE. RETRIEVING APPROPRIATE IMAGES FROM AN
IMAGE DATABASE IS ONE OF THE DIFFICULT TASKS IN MULTIMEDIA TECHNOLOGY. OUR RESULTS SHOW THAT THE
VALUES OF RECALL AND PRECISION CAN BE ENHANCED AND THIS ALSO SHOWS THAT SEMANTIC GAP CAN ALSO BE
REDUCED. THE PROPOSED ALGORITHM ALSO EXTRACTS THE TEXTURE VALUES FROM THE IMAGES AUTOMATICALLY
WITH ALSO ITS CATEGORY (LIKE SMOOTH, COURSE ETC) AS WELL AS ITS TECHNICAL INTERPRETATION.
Similar to Content-Based Image Retrieval Features: A Survey (20)
The cyber attacks have become most prevalent in the past few years. During this time, attackers have discovered new vulnerabilities to carry out malicious activities on the internet. Both the clients and the servers have been victimized by the attackers. Clickjacking is one of the attacks that have been adopted by the attackers to deceive the innocuous internet users to initiate some action. Clickjacking attack exploits one of the vulnerabilities existing in the web applications. This attack uses a technique that allows cross domain attacks with the help of userinitiated clicks and performs unintended actions. This paper traces out the vulnerabilities that make a website vulnerable to clickjacking attack and proposes a solution for the same.
Performance Analysis of Audio and Video Synchronization using Spreaded Code D...Eswar Publications
The audio and video synchronization plays an important role in speech recognition and multimedia communication. The audio-video sync is a quite significant problem in live video conferencing. It is due to use of various hardware components which introduces variable delay and software environments. The objective of the synchronization is used to preserve the temporal alignment between the audio and video signals. This paper proposes the audio-video synchronization using spreading codes delay measurement technique. The performance of the proposed method made on home database and achieves 99% synchronization efficiency. The audio-visual
signature technique provides a significant reduction in audio-video sync problems and the performance analysis of audio and video synchronization in an effective way. This paper also implements an audio- video synchronizer and analyses its performance in an efficient manner by synchronization efficiency, audio-video time drift and audio-video delay parameters. The simulation result is carried out using mat lab simulation tools and simulink. It is automatically estimating and correcting the timing relationship between the audio and video signals and maintaining the Quality of Service.
Due to the availability of complicated devices in industry, models for consumers at lower cost of resources are developed. Home Automation systems have been developed by several researchers. The limitations of home automation includes complexity in architecture, higher costs of the equipment, interface inflexibility. In this paper as we have proposed, the working protocol of PIC 16F72 technology is which is secure, cost efficient, flexible that leads to the development of efficient home automation systems. The system is operational to control various home appliances like fans, Bulbs, Tube light. The following paper describes about components used and working of all components connected. The home automation system makes use of Android app entitled “Home App” which gives
flexibility and easy to use GUI.
Semantically Enchanced Personalised Adaptive E-Learning for General and Dysle...Eswar Publications
E-learning plays an important role in providing required and well formed knowledge to a learner. The medium of e- learning has achieved advancement in various fields such as adaptive e-learning systems. The need for enhancing e-learning semantically can enhance the retrieval and adaptability of the learning curriculum. This paper provides a semantically enhanced module based e-learning for computer science programme on a learnercentric perspective. The learners are categorized based on their proficiency for providing personalized learning environment for users. Learning disorders on the platform of e-learning still require lots of research. Therefore, this paper also provides a personalized assessment theoretical model for alphabet learning with learning objects for
children’s who face dyslexia.
Agriculture plays an important role in the economy of our country. Over 58 percent of the rural households depend on the agriculture sector as their means of livelihood. Agriculture is one of the major contributors to Gross Domestic Product(GDP). Seeds are the soul of agriculture. This application helps in reducing the time for the researchers as well as farmers to know the seedling parameters. The application helps the farmers to know about the percentage of seedlings that will grow and it is very essential in estimating the yield of that particular crop. Manual calculation may lead to some error, to minimize that error, the developed app is used. The scientist and farmers require the app to know about the physiological seed quality parameters and to take decisions regarding their farming activities. In this article a desktop app for seed germination percentage and vigour index calculation are developed in PHP scripting language.
What happens when adaptive video streaming players compete in time-varying ba...Eswar Publications
Competition among adaptive video streaming players severely diminishes user-QoE. When players compete at a bottleneck link many do not obtain adequate resources. This imbalance eventually causes ill effects such as screen flickering and video stalling. There have been many attempts in recent years to overcome some of these problems. However, added to the competition at the bottleneck link there is also the possibility of varying network bandwidth which can make the situation even worse. This work focuses on such a situation. It evaluates current heuristic adaptive video players at a bottleneck link with time-varying bandwidth conditions. Experimental setup includes the TAPAS player and emulated network conditions. The results show PANDA outperforms FESTIVE, ELASTIC and the Conventional players.
WLI-FCM and Artificial Neural Network Based Cloud Intrusion Detection SystemEswar Publications
Security and Performance aspects of cloud computing are the major issues which have to be tended to in Cloud Computing. Intrusion is one such basic and imperative security problem for Cloud Computing. Consequently, it is essential to create an Intrusion Detection System (IDS) to detect both inside and outside assaults with high detection precision in cloud environment. In this paper, cloud intrusion detection system at hypervisor layer is developed and assesses to detect the depraved activities in cloud computing environment. The cloud intrusion detection system uses a hybrid algorithm which is a fusion of WLI- FCM clustering algorithm and Back propagation artificial Neural Network to improve the detection accuracy of the cloud intrusion detection system. The proposed system is implemented and compared with K-means and classic FCM. The DARPA’s KDD cup dataset 1999 is used for simulation. From the detailed performance analysis, it is clear that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate.
Spreading Trade Union Activities through Cyberspace: A Case StudyEswar Publications
This report present the outcome of an investigative research conducted to examine the modu-operandi of academic staff union of polytechnics (ASUP) YabaTech. The investigation covered the logistics and cost implication for spreading union activities among members. It was discovered that cost of management and dissemination of information to members was at high side, also logistics problem constitutes to loss of information in transit hence cut away some members from union activities. To curtail the problem identified, we proposed the
design of secure and dynamic website for spreading union activities among members and public. The proposed system was implemented using HTML5 technology, interface frameworks like Bootstrap and j query which enables the responsive feature of the application interface. The backend was designed using PHPMYSQL. It was discovered from the evaluation of the new system that cost of managing information has reduced considerably, and logistic problems identified in the old system has become a forgotten issue.
Identifying an Appropriate Model for Information Systems Integration in the O...Eswar Publications
Nowadays organizations are using information systems for optimizing processes in order to increase coordination and interoperability across the organizations. Since Oil and Gas Industry is one of the large industries in whole of the world, there is a need to compatibility of its Information Systems (IS) which consists three categories of systems: Field IS, Plant IS and Enterprise IS to create interoperability and approach the
optimizing processes as its result. In this paper we introduce the different models of information systems integration, identify the types of information systems that are using in the upstream and downstream sectors of petroleum industry, and finally based on expert’s opinions will identify a suitable model for information systems integration in this industry.
Link-and Node-Disjoint Evaluation of the Ad Hoc on Demand Multi-path Distance...Eswar Publications
This work illustrates the AOMDV routing protocol. Its ancestor, the AODV routing protocol is also described. This tutorial demonstrates how forward and reverse paths are created by the AOMDV routing protocol. Loop free paths formulation is described, together with node and link disjoint paths. Finally, the performance of the AOMDV routing protocol is investigated along link and node disjoint paths. The WSN with the AOMDV routing protocol using link disjoint paths is better than the WSN with the AOMDV routing protocol using node disjoint paths for energy consumption.
Bridging Centrality: Identifying Bridging Nodes in Transportation NetworkEswar Publications
To identify the importance of node of a network, several centralities are used. Majority of these centrality measures are dominated by components' degree due to their nature of looking at networks’ topology. We propose a centrality to identification model, bridging centrality, based on information flow and topological aspects. We apply bridging centrality on real world networks including the transportation network and show that the nodes distinguished by bridging centrality are well located on the connecting positions between highly connected regions. Bridging centrality can discriminate bridging nodes, the nodes with more information flowed through them and locations between highly connected regions, while other centrality measures cannot.
Now a days we are living in an era of Information Technology where each and every person has to become IT incumbent either intentionally or unintentionally. Technology plays a vital role in our day to day life since last few decades and somehow we all are depending on it in order to obtain maximum benefit and comfort. This new era equipped with latest advents of technology, enlightening world in the form of Internet of Things (IoT). Internet of things is such a specified and dignified domain which leads us to the real world scenarios where each object can perform some task while communicating with some other objects. The world with full of devices, sensors and other objects which will communicate and make human life far better and easier than ever. This paper provides an overview of current research work on IoT in terms of architecture, a technology used and applications. It also highlights all the issues related to technologies used for IoT, after the literature review of research work. The main purpose of this survey is to provide all the latest technologies, their corresponding
trends and details in the field of IoT in systematic manner. It will be helpful for further research.
Automatic Monitoring of Soil Moisture and Controlling of Irrigation SystemEswar Publications
In past couple of decades, there is immediate growth in field of agricultural technology. Utilization of proper method of irrigation by drip is very reasonable and proficient. A various drip irrigation methods have been proposed, but they have been found to be very luxurious and dense to use. The farmer has to maintain watch on irrigation schedule in the conventional drip irrigation system, which is different for different types of crops. In remotely monitored embedded system for irrigation purposes have become a new essential for farmer to accumulate his energy, time and money and will take place only when there will be requirement of water. In this approach, the soil test for chemical constituents, water content, and salinity and fertilizer requirement data collected by wireless and processed for better drip irrigation plan. This paper reviews different monitoring systems and proposes an automatic monitoring system model using Wireless Sensor Network (WSN) which helps the farmer to improve the yield.
Multi- Level Data Security Model for Big Data on Public Cloud: A New ModelEswar Publications
With the advent of cloud computing the big data has emerged as a very crucial technology. The certain type of cloud provides the consumers with the free services like storage, computational power etc. This paper is intended to make use of infrastructure as a service where the storage service from the public cloud providers is going to leveraged by an individual or organization. The paper will emphasize the model which can be used by anyone without any cost. They can store the confidential data without any type of security issue, as the data will be altered
in such a way that it cannot be understood by the intruder if any. Not only that but the user can retrieve back the original data within no time. The proposed security model is going to effectively and efficiently provide a robust security while data is on cloud infrastructure as well as when data is getting migrated towards cloud infrastructure or vice versa.
Impact of Technology on E-Banking; Cameroon PerspectivesEswar Publications
The financial services industry is experiencing rapid changes in services delivery and channels usage, and financial companies and users of financial services are looking at new technologies as they emerge and deciding whether or not to embrace them and the new opportunities to save and manage enormous time, cost and stress.
There is no doubt about the favourable and manifold impact of technology on e-banking as pictured in this review paper, almost all banks are with the least and most access e-banking Technological equipments like ATMs and Cards. On the other Hand cheap and readily available technology has opened a favourable competition in ebanking services business with a lot of wide range competitors competing with Commercial Banks in Cameroon in providing digital financial services.
Classification Algorithms with Attribute Selection: an evaluation study using...Eswar Publications
Attribute or feature selection plays an important role in the process of data mining. In general the data set contains more number of attributes. But in the process of effective classification not all attributes are relevant.
Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
Network as a Service Model in Cloud Authentication by HMAC AlgorithmEswar Publications
Resource pooling on internet-based accessing on use as pay environmental technology and ruled in IT field is the
cloud. Present, in every organization has trusted the web, however, the information must flow but not hold the
data. Therefore, all customers have to use the cloud. While the cloud progressing info by securing-protocols. Third
party observing and certain circumstances directly stale in flow and kept of packets in the virtual private cloud.
Global security statistics in the year 2017, hacking sensitive information in cloud approximately maybe 75.35%,
and the world security analyzer said this calculation maybe reached to 100%. For this cause, this proposed
research work concentrates on Authentication-Message-Digest-Key with authentication in routing the Network as
a Service of packets in OSPF (Open Shortest Path First) implementing Cloud with GNS3 has tested them to
securing from attackers.
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Content-Based Image Retrieval Features: A Survey
1. Int. J. Advanced Networking and Applications
Volume: 10 Issue: 01 Pages: 3741-3757 (2018) ISSN: 0975-0290
3741
Content-Based Image Retrieval Features: A
Survey
Anum Masood
Department of Computer Science, COMSATS Institute of Information Technology, WahCantt, Pakistan
[E-mail: anum_msd21@yahoo.com]
Muhammad Alyas Shahid
Department of Computer Science, COMSATS Institute of Information Technology, WahCantt, Pakistan
[E-mail: mashahid79@gmail.com]
Muhammad Sharif
Department of Computer Science, COMSATS Institute of Information Technology, WahCantt, Pakistan
[E-mail: muhammadsharifmalik@yahoo.com]
-------------------------------------------------------------------ABSTRACT-----------------------------------------------------------
Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various
research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is
the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram,
color moments, conventional color correlogram and color histogram. Color space selection is used to represent
the information of color of the pixels of the query image. The shape is the basic characteristic of segmented
regions of an image. Different methods are introduced for better retrieval using different shape representation
techniques; earlier the global shape representations were used but with time moved towards local shape
representations. The local shape is more related to the expressing of result instead of the method. Local shape
features may be derived from the texture properties and the color derivatives. Texture features have been used
for images of documents, segmentation-based recognition,and satellite images. Texture features are used in
different CBIR systems along with color, shape, geometrical structure and sift features.
Keywords: CBIR, Feature Extraction, Feature Selection, Color, Texture, Shape
--------------------------------------------------------------------------------------------------------------------------------------------------
Date of Submission: April 06, 2018 Date of Acceptance: April 25, 2018
--------------------------------------------------------------------------------------------------------------------------------------------------
I. INTRODUCTION:
With more digital applications, the number of images
produced is increasing rapidly. Huge databases of images
have to be searched to retrieve related images; this is
mentioned as CBIR techniques. A manual search of such
image databases is not only exhaustive but is also not
efficient therefore various approaches have been presented
for the improvement of images retrieval. Accurate
retrieval of images from a lot of digital images is
challenging, therefore, CBIR is considered as an intensive
research area [1-3]. Previously used text-based query
methods are not as efficient and effective as feature-based
methods. The various primitive features such as color,
shape, structure, texture, etc can be used to extract the
similarities between the images already stored in database
and a query by user [4-8].
These features are called low-level features which are
utilized in the search for feature extraction images to
match to the large image database [9-10]. There are
various CBIR systems that use more than one feature.
Color is used in combination with both texture and shape
for improved results [11-12].
1. Applications of CBIR:
CBIR is being used in various fields like biomedicine [13-
14], medicine [15], military [16], and commerce [17], Art
[18,19], Art Galleries [20-21] and Entertainment like
content-basedmultimedia retrieval. For Law enforcement
[22], CBIR is used for face-detection [23-24] copy-
detection [25] while for the control of cyber-crime, CBIR
is used in Image-based CAPTCHA [26-27].In the field of
bioinformatics, it is used for Automatic Annotation [28],
molecular analysis [29], medical diagnosis [30-31] and
face recognition [32-54]. The search engines also use
CBIR for web-search for images [55]. CBIR is widely
being used in biometrics systems. In recent years use of
CBIR systems in various fields has been increased.
In this survey paper, a comprehensive overview of the
CBIR is presented. In Section 2, CBIR Systems are
discussed. Various problem domains of CBIR system are
also mentioned along with the classification of CBIR
systems. Section 3 gives classification is CBIR systems. In
Section 4, few color based techniques used in CBIR
systems are discussed. Techniques using the shape
featurein CBIR systems are mentioned in Section 5. The
summary of various texture based methods over the few
years is shown in Section 6.
II. CBIR SYSTEM:
In any CBIR system, there are mainly three phases i.e.
feature extraction from query image, feature selection and
then the similarity matching. The multi-dimensional
feature vectors are created by the CBIR system and these
feature vectors are matched with those of database images.
The similarity measure or distance measure is done with
the help of some algorithm on which score is calculated.
The threshold value is pre-defined by the system
2. Int. J. Advanced Networking and Applications
Volume: 10 Issue: 01 Pages: 3741-3757 (2018) ISSN: 0975-0290
3742
developer. Those images which have more score than the
threshold value are extracted and shown as retrieved
results. The Relevance feedback method is adopted so that
whether the user is satisfied or not satisfied by the
retrieved images then the CBIR system is improved to
search keeping in view the selected images by the user.
In Figure 1, a flowchart of CBIR system is shown:
Figure 1: Flow-diagram for CBIR System [69]
2.1. Image Content Descriptor:
An image content Descriptor can be local or global. It can
be specific as well as general. Global uses features of
thewhole image and local divides image into parts first. A
simple method of partition is to use a division i-e cut
image into regions having equal shape and size. They may
not be meaningful and significant regions but it is a
process to represent global features of any image. Partition
the image into similar and homogeneous areas is an
improved method with the use of some standard such as
the Region Segmentation algorithms. Another complex
method is to obtain semantically meaningful objects by
Object Segmentation. The image content is further
classified into two broad categories as visual content and
semantic content.
2.1.1. Visual Content:
The visual content is further classified into two main
classes:
2.1.1.1. General Visual Content:
When the features or content of the query image are
visible and are generally perceived then they fall into this
category. Included common visual contents are the
features like shape, texture, color, structure, spatial
relationship etc.
2.1.1.2. Domain Specific Visual Content:
When the query is based on such content that requires
some domain knowledge then those query image content
are domain specific visual content like Human Face
Detection needs some prior information about the human
facial characteristics. These characteristics are not general;
these are specific i-e only related to human facial features.
2.1.2. Semantic Content:
The semantic contentsare either described by textual
explanation or by using the complex interpretationmeans
which are based on some visual-content.
2.2. Image Retrieval Gaps:
The differences between images stored in database and the
query image in retrieval are called gaps. The degree of
difference will be how far the two images are i-e the gap
between the two images. The gaps are divided into two
categories: semantic and sensory.
2.2.1. Semantic Gaps:
The mismatching of information of visual query data and
the stored image information in the database is obtained.
This selected gape to match the image on the similarities
basis is called semantic gap. User entered some queries for
which optical likeness does not match completely with
human perception. By which a semantic gap between
CBIR system and the user is obtained [56]. Semantic
retrieval has some limitations. A difficulty present in it is
that most of the images have more than one semantic
interpretation. Because images used for training have
usually short description in form of a caption, therefore,
some features might never be recognized. This helps to
decrease the amount of images instances used for training
and weakens the system’s capability to be trained for the
concepts that are rare and which have a high variable
visual appearance. Semantic retrieval system has a limited
vocabulary so it mostly generalizes everything other than
the semantic space i.e. for which is not trained [57].
2.2.2. Sensory Gaps:
These are the gaps between the real object and the
information in form of computational description obtained
from capturing that object in an image form. It is the short-
coming of the image capturing device.
2.3. Problem Domains:
Retrieving images from a varied and huge collection is
difficult. Searching images based on the spatial
relationship of portions of a query image is a not an easy
research problem in CBIR. As segmentation of regions or
objects of an image is not feasible, except in few
applications. Another common problem in CBIR is a
selection of Features.
The divergence between user’s query and the result
displayed by retrieval system images is called Semantic
gap. These semantic gaps are caused due to the low-level
similarity between database images and high-level user-
query image this result in an error from search algorithms.
Image
Collection
Extraction
of Features
Feature
Database
Query
Image
Extraction
of Features
Query Image
Features
Database
Query Image
Features
Retrieved
Image
Feature
Matching
3. Int. J. Advanced Networking and Applications
Volume: 10 Issue: 01 Pages: 3741-3757 (2018) ISSN: 0975-0290
3743
RF (Relevance Feedback) is the solution for thesame
problem in which the user has the authority to accept or
reject the result and requests for better similarity in output
images.
2.4. CBIR Methods:
There are different methods used in CBIR. Figure 2
represents the most commonly used methods by the CBIR
systems for the image retrieval.
The Features extraction is mostly the initial step in any
CBIR system. Each feature selected is placed in a vector
named Feature Vector. For Feature Extraction some of the
techniques used are the Symbol spotting (graphical shape
extraction), [58] background removal approach [59] key-
points technique [60]. Some other techniques are the usage
of Color, texture, shape, structure, edges as features.
Figure 2: Methods of CBIR Systems [60]
Another method is by the use of Salient region features of
the user-defined image. Few examples are the use of the
global features of the image [61]. Global features such as
the interest points of query image as well as SIFT
descriptor are used in CBIR [62-64]. Image Segmentation
is also done for better region selection. Common methods
are IMALBUM [65], SIMPLIcity, [66] Watershed method
and Region growing method (bottom-up).
Figure 3: Classifications of CBIR Systems [67]
III. CBIR SYSTEM CLASSIFICATION:
As the CBIR systems are increasing in number there was a
need for the classification of these systems. One
classification of CBIR systems is shown in Figure
3.Feature such as texture, color, and shape are most
frequently used in feature extraction phase of CBIR
Systems. The growing need for effective as well as
efficient image retrieval has made the selection of an
appropriate set of features as the most critical step in the
CBIR system formation. In image retrieval research is
usually related to the statistical or general methods for the
properties of the patches of the image.
IV. COLOR FEATURE:
When viewing an image one of the basic features we
observe is color. Color reveals a variety of information.
For usage of color as a feature in CBIR, there has been a
lot of research done such as the color histogram [68-69],
conventional color histogram [70-73], color correlogram,
and color moments as shown in Figure 4.
4.1 Color Space:
A point in any 3D color space is represented in form of a
pixel. Color spaces like RGB, CMY, and HSV (or HSL,
HSB) are widely used. Which color space is the best; there
is no consensus for results of images retrieved.
4.1.1 RGB (Red, Green and Blue) Color Space:
In color combination of RGB each pixel has three values
which are added together to represent a single pixel value
in the color space RGB. Another name for RGB is
Additive Primaries as they are added together for giving
single pixel value. It is device-dependent and non-uniform.
4.1.2 CMY (Cyan, Magenta and Yellow) Color Space:
CMY consist of three color combinations which are Cyan,
Magenta and Yellow. Each pixel has three values which
are subtracted to represent a single pixel value in CMY. In
CMY color space is formed through absorption of light. It
is dependent on the device used and visually non-uniform.
Extraction of Feature Similarity Measurements
Using Salient Regions of
the image
Retrieval based on
Clustering techniques
CBIR Methods
CBIR System
Classification
Query Form (database
image retrieval on basis
of a query)
Image Description
(images stored with
description)
User interactive CBIR
system (User's Authority
for images classification)
Database Content
(Database of CT-Scan
images)
4. Int. J. Advanced Networking and Applications
Volume: 10 Issue: 01 Pages: 3741-3757 (2018) ISSN: 0975-0290
3744
4.1.3 HSV (hue, saturation (illumination) and value
of brightness) Color Space:
In computer graphics the most used color space is HSV.
The Hue is more suitable for retrieval of an object as it is
not affected (invariant) to the deviations in light intensity
and direction of the camera. A simple formula [74] can
convert the RGB coordinates for an image into HSV
coordinates.
4.1.4 Opponent Color Space:
It the three axes obtain as R-G, 2B-R-G, R+G+B. The
third axis isolates the information about the brightness
which is the advantage of opponent color space. As human
is sensitive to brightness, therefore, we require a color
space which considers the brightness. Opponent Color
Space is not invariant to light intensity and shadows hence
it is better than the HSV color space.
4.2 Uniformity:
When a color space is selected, uniformity a most
important feature is observed [75]. It means that the
viewer observes the two color pairs as the same color with
same similarity distance. Thus the distance measured
among colors should be directly proportional to the
perceptual similarity between them.
4.3 Color Descriptor:
CBIR color descriptor can be local or global. Color space
selection is important for the color descriptor. As a color
space is selected for the representation of the information
of color of a pixel of the query image. Quantization of
color-space means those colors which are very close to
each other and have small distance should be combined
together.
There are certain phrases which are followed when color is
used as the descriptor. First of all, a color space is
designated. Secondly, the quantization of the color space
is completed. After quantization color features are
extracted. These color features collectively are known as a
color descriptor. An appropriate distance function is
derived or any particular CBIR System.
Figure 4: Phases of Color Feature for CBIR [72]
4.4 Color Histogram (CH):
An effective and real representation is a color histogram.
A pixel may be represented as 3 components in the
histogram of color space. For the color histogram, spatial
information about histogram is not considered. This
simple approach is used to split the sub-areas for
histogram calculation [76-78]. The histogram is a
statistical global feature which shows distribution
intensity. Color histograms are widely used in CBIR. [79].
Color feature is often used in a combination of texture and
structure features [80]. Color Histograms are also useful in
recognition of objects. In a large number of images, the
matching of the histogram may saturate discrimination.
Joint Histogram is the solution [81]. Different color
histograms methods have been used for image retrieval.
Few of the color histogram methods are described below:
4.4.1 Conventional Color Histogram (CCH)
It shows the frequency of occurrence of each color (no. of
pixels having that particular color) within a given image.
Every color is connected to its histogram on basis of color
similarity. Dissimilarity basis is not taken into account.
To reduce the number of the histograms, Quantization is
done. Quantization means to merge two colors histograms
which have a similar color. Thus processing time is
lessened. Figure 5 shows the CCH steps.
Figure 5: Different Steps of Conventional Color Histogram [73]
Color Space Selection
Color Space Quantization
Color Feature Extraction
appropriate Distance Function
Derivation
Quantization of
Bins if small color
difference
Frequency
Calculation by
Counting number
of pixel for each Bin
Color Space Division
into Bins
5. Int. J. Advanced Networking and Applications
Volume: 10 Issue: 01 Pages: 3741-3757 (2018) ISSN: 0975-0290
3745
4.4.2 Invariant Color Histograms (ICH)
Invariant Color Histogram is developed on the basis of
color gradients. As the name suggests that it is invariant
under any geometry of surface which is locally affine [82].
4.4.3 Fuzzy Color Histogram (FCH):
During merging of different color histograms with the
slight difference a problem arises. The color may appear to
be similar yet they have a small difference. To overcome
this problem FCH concept was introduced. The color
space is used for relative greenness-redness and blueness-
yellowness) [83].
It also defines the extent of there semblance of the color of
a pixel cube for all histograms fuzzy relationship function
set given by𝜇 . It is stronger to quantization
complications as the changes in in the density of light. The
drawbacks are that it describes only the FCH features and
the global color feature of the image is equally high in
dimensionality as CCH. There is an extra computation for
fuzzy membership function𝜇 .
4.4.4 Color Difference Histogram (CDH):
This is a recent technique for the image color feature
representation [84]. Previously the histogram techniques
only counted the number of frequency i-e the occurrence
of the pixels. CDH takes the total of perceptual difference
in uniform colors between any two points of images under
various backgrounds along with the edge orientation and
color in the color space L*a*b*.
4.4.4.1 Block Encoding of Color Histogram for CBIR
Applications:
Color is a low-level feature yet important feature which is
mostly represented as a histogram in different CBIR
systems. There are different coding approaches for
histogram so that the space used is less and processing is
speedy. Recent research on encoding approaches is done
by [85]. They have used the Golomb-Rice coding which
codes values of the color histogram in floating point by
converting them to integers in some pre-processing steps.
The histogram is mostly shown in the sparse matrix.
4.5 Color Coherence Vector (CCV):
In CCV [86] spatial information is included in a histogram
in a diverse manner. A histogram bin may be classified
into two classes i-e Coherent (used for the uniformly-
colored large region) or incoherent (not used for the
uniformly-colored large region). CCV of an image can be
defined as follows in vector form as in equation – (1):
< , , , , … . . , 𝑁, 𝑁 > (1)
CCV has additional information as compared to Color
Histogram, therefore, it gives better retrieval results.
4.6 Color Correlogram (CC):
It shows the association of colors in terms of distance. CC
is presented as indexed in a table by sets of color where
the probability of occurrence of any pixel of color j at a
distance d from a pixel of I in the given image is
represented by the entry for row (i, j) [87].
The space between pixels P1 and P2 is given by in
equation – (2):
|𝑝 − 𝑝 | (2)
The d with a small value is enough to show special
correlations as local correlations are more important than
global correlation.
CC shows local as well as global spatial information. The
disadvantage of CC is that it has high-dimensionality of
feature space. For reduction of this dimensionality from
(O(N2
d)) to (O(Nd)) a new feature was introduced. It is
known as auto-color correlogram. Auto-correlogram
confines the spatial correlation of identical levels only
which is shown as given in equation – (3)
𝑑
𝐼 = 𝑔,𝑔
𝑑
𝐼 (3)
4.7 Color Moment (CM):
CMare using frequently in CBIR systems which are based
on color features. CM of First Order is normally mean,
Second Order is Variance and the third order is mainly
skewness. These are used in representing color
distributions.
4.7.1 Mean - First Order Color Moment:
It is defined by the equation–(4):
𝜇 =
𝑁
∑ 𝑓𝑁
= (4)
4.7.2 Variance - Second Order Color Moment:
Second order color moment is given by the equation - (5)
given below:
𝜎 =
𝑁
∑ 𝑓 − 𝜇𝑁
= (5)
4.7.3 Skewness - Third Order Color Moment:
Color moment third order (skewness) is given in equation
(6):
=
𝑁
∑ 𝑓 − 𝜇𝑁
= (6)
The value of the ith-color component of any image pixel j
is given by fij and the total no. of pixels of an image are
represented by N. Color Moment is a compact feature as
only three moments for each of the three color component
are required i-e a total of nine values is needed. Although
it is compact yet there is a problem of lower
discrimination power.
4.8 Invariant Color Features:
Color changes with the change in illumination [88].
Variability is considered [89]. Invariance to such
environmental factor is not regarded as part of most color
feature [90-91] Equation for the invariant color feature is
as follows given in equation –(7):
𝑅−𝐺
𝑅+𝐺
,
𝐵−𝑅
𝐵+𝑅
,
𝐺−𝐵
𝐺−𝐵
(7)
It only depends on the surface and the sensor. For shiny
surfaces its equation is given as equation-(8):
|𝑅−𝐺|
|𝑅−𝐺|+|𝐵−𝑅|+|𝐺−𝐵|
(8)
This color-shaped based method considers the color, area
and a new feature called perimeter-intercepted lengths of
6. Int. J. Advanced Networking and Applications
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3746
the object-segmentation in a given image [92].The image
is segmented into K clusters with the help of K-means
algorithm. The object’s shape is distinguished by the PILs
(perimeter-intercepted lengths). PILs are acquired by the
interception of 8 line-segments from the object perimeter.
All these 8 lines have different orientations yet pass
through the center of the object [93].
Table 1: Comparison of Different Techniques using color as main feature
Sr # Techniques Advantages Limitations
1. CCH [94] Simple
Computation is easy
Sensitive to noise
High-dimensionality problem
Cannot handle rotation and translation
Global spatial information is not encoded
2. ICH[95] Solves the problem of translation and
rotation in image
Invariant under any geometry of the
surface
3. FCH
[96-97]
Encodes degree of similarity of pixel
color by fuzzy-set relationship function
Robust to quantization problems
Robust to light intensity changes
Describes global color properties of the
image only
FCH features are equally high in
dimensionality as CCH
Extra computation for the fuzzy
membership function
4. CCV[98] CCV spatial information
Gives better retrieval results.
Only categorize into two partitions i-e
coherent or incoherent
5. CC[99] Encodes global as well as local spatial
information
Works even for coarse color images
High-dimensionality of feature space
6. CM[100-101] Compact as only nine values is needed
for calculation
Less computation
Compact but lower discrimination power
7. Color-Shape Based
Method (CSBM) [102]
Encodes object shapes and colors More computation
Requires an appropriate color threshold
Sensitive to noise variation
Sensitive to contrast
4.10 Color Image Retrieval Technique based on
Color Feature:
From a database of images, for retrieval of similar images,
an effective and efficient technique was used by [103].
Some color distributions are used with standard and mean
deviation for representation of the global features in any
image. The local characteristics of the image were
represented by a bitmap of the image. This local
characterization increased the accuracy of the image
retrieval system.
V. SHAPE FEATURE:
A basic aspect of segmented regions of an image is its
shape feature. Its representation is important in the
retrieval of similar images [11]. The shape characteristics
are extracted and matched with the database image
collection, therefore; the shape feature representation is
most significant [10].
There are different methods introduced to help in better
retrieval by using different shape representation
techniques [104]. Earlier the global shape representations
were used but with time they move towards local shape
representations as local descriptors produce better retrieval
results [105-106]. Shape representation using discrete
curve evolution for contours simplification removes
irrelevant shape features [107].
5.1. Shape Context (Shape Descriptor):
For similarity matching a shape descriptor called shape
context was proposed which was useful for varying
geometric transformations [108]. Shapes approximation as
concave and convex segments was the approach of
Dynamic Programming for shape matching. The only
problem with it was that there was pre-computation for the
Fourier Descriptor and moments in this technique which
was slow [109].
5.2. Local Shape Features:
In the local shape feature, all the characteristics related to
the geometric details of an image [110]. The local shape is
more related to the expressing of result instead of the
method [111]. Local shape features may be derived from
the texture properties and the color derivatives.
5.3. Scale-Space Theory for Shape Feature:
Scale-Space theory was advised for capturing all the
prominent information, [112]. It gives the basis for the
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detection and recognition of some prominent features and
details on any scale.
5.4. Shape Affine-Invariant Descriptor:
Differential geometric invariant and affine-invariant
descriptor are also available [113]. Color feature and shape
have been combined in invariant method [114-115].
5.5. Object-Shape Feature:
Theoretically, the best approach by segmenting the object
for the object-specific information of imagesis within the
image [116]. It is not necessary to recognize the exact
location of features of the object in an image but it is
important to identify the presence of an object with the
help of its properties. When segmentation is done as
shown in the equation–(9)
𝑥 = °𝑓 𝑥 (9)
The shape of an object is shown in equation-(10):
𝐹 = ∑ ℎ° 𝑥𝑇 𝑗
(10)
Where h shows the functional computing shape and ∑
show the aggregation operation. This equation is for the
accumulative features. Some features have general
applicability like moment invariants and the Fourier
features of objects [117].
For the image retrieval, there is a requirement for the
shape representation that measures the distances of
deformations. Some mismatch images are acceptable in
certain interactive use of retrieval. So compromise on
accuracy may be possible as we need a system to be robust
and computationally efficient [118]. There are
sophisticated methods like elastic deformation of image
portions, multi-resolution representation of shapes and
modal matching techniques [119-120].
5.6. Contour Multi-scale Model:
Multi-scale models of contours have been studies for
database image representation. There are two approaches
to Contour multi-scale model:
a) Multi-Scale Model of Contours as Database
Image Representation
b) Multi-resolution Shape Model for Contours.
These two approaches for contours use are shown in Fig 6.
Figure 6: (a) Steps of a Multi-Scale Model of Contours as Database Image Representation (b) Steps of Multi-resolution
Shape Model for Contours. [121]
5.7. Contour Representation:
The images have to process for retrieval which comprises
a group of operations on the contours. Edge-contours are
represented by the B-spline. There are three detection
types of symmetries: Skew and parallel symmetries are
used for inferring shape from the contour. The third type
of symmetry is smooth local symmetries used for planar
shape description [121].
VI. TEXTURE FEATURE:
In Image processing as well as in computer graphics and
computer vision the texture features are widely used [122]
like wavelet transforms, discrete cosine transform [123]
and curvelet transform. Many textures are composed of the
small textons which are numerous and thus cannot be
perceived as separate objects.
6.1 Structural Pattern Feature:
Various texture features are proposed for analyzing the
structural pattern information in an image. These texture
features include Co-Occurrence of gray-scale values [124],
multi-resolution analysis [125]; Local Pattern Spectrum
[96] and texture unit spectrum-based [126-127]
techniques.
6.2 Gabor-Filter Based Techniques for Texture
Feature:
Popular Techniques include Gabor-filter based techniques
[128-129] Wavelet Decomposition with energy signatures
[130] and Gaussian Markov random field (GMRF)
models. The query image is afterward convolved with
each Gabor functions obtained after the parameter settings.
It has been shown that for the highest texture retrieval
results, Gabor Transform for texture feature is used in
Multi-Scale Model
Contours Extracted from images
Contours smoothed by dividing into
regions (Using second derivative)
Reducing the number of regions
Every contour reduced to an ellipsoid for
characterization
Multi-Resolution Shape Model
Contours sampled by polygon
Simplified by removing points from
contours
Set of Contours points on the boundaries
selected
Point Pairs used for geometrically
invariant contours representation
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CBIR [131]. Results of the Gabor Transform function is
an over‐complete demonstration of the user’s query image
with a certain redundant ratio of K*S (scales*orientation).
6.3 Modified Gabor Function:
Gabor transform has to be applied from all orientations as
it depends on the direction. The angles used have an effect
on the results of the image retrieval. Therefore the existing
Gabor transform had to be modified. The modified Gabor
transform is independent of the angle selection. Its
performance is almost equal to the simple Gabor
Transform.
The modified Gabor Transform is invariant to the rotation.
Because of this invariance for rotation, the modified Gabor
Transform showed better results in almost all the images
[132].
6.3.1 Contourlet Transform for Texture Features:
These are based on directional multi scale filter bank
which deals with the smooth contours of the images.
Contourlets allow various directions at every scale which
in turns gives a critical sampling. They give directionality
as well as wavelets features. Figure 7 expresses the
comparisons of Gabor Wavelet Transform Method and
Contourlet Transform approach is shown.
Figure 7: Comparison of Gabor Wavelet Transform Method and Contourlet Transform Method [127]
6.3.2 Gray-level Run Length Technique for Texture
Feature:
Gray-level run length is useful when analysis at more than
single orientation is to be done for the extraction of
structural information. Spectrum-based techniques are also
not recent [133]. With time signal-processing has attained
progress which results in the efficient power spectrum
tools for analysis. Earlier techniques were gray-level run
length [134] gray-level co-occurrence matrix [122]. Some
models utilize statistical regularities in the texture field
while other models take texture as result of a deterministic
dynamic system which observes noise.
6.4 Techniques for Transformations of Texture
Surfaces:
A collective problem is that for the texture feature
extraction the image with texture surfaces has to undergo
numerous scaling and re-orientation steps but it is a
computationally difficult task. Previously Pyramid
Structured Wavelets [135] tree-structured wavelets [136];
multi-resolution simultaneous autoregressive models were
the multi-resolution technique but the Gabor filters have
shown better performance [137].
6.4.1 Techniques for Illumination Effect on Texture
Surfaces:
The recent algorithms proposed for the texture analysis
consider the non-ideal conditions like illumination
intensity change due to the source itself or the capturing
device, texture viewing angle change and spectral
attributes of light [138]. In the real-world scenario, there
are certain environmental factors like changes of light
directed from source [139]. Changes in the spectral
attributes of light also have an effect on the texture of the
image surface affecting the inter-class distances as well as
the intra-class of the textures [140].
Gabor Wavelet Transform
S Scales and K orientations
Rotation and dilations of 2D
Gabor function
obtained K*S Gabor Functions
Image convolvolation with
Gabor Function
Representation of original
image with K*S redundant
ratio
Contourlet Transform
Bandpass images from
Laplacian Pyramid
Fed to Directional Filter Bank
Directional information
obtained
Low frequency components
separated
Redundancy ratio is less as
directional sub-bands are
decimated
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3749
6.4.2 Algorithms for Changes in Affine
Transformation and Light Attribute:
Affine Transformations like rotation and scaling of images
affect the texture surfaces [141-142], the non-affine
transformations like 3D orientation changes also affect
texture characteristics [143]. In the real-world, the 3D
nature of the textured surface may significantly change the
observed texture data by light direction change [144].
Some of the recent texture analysis algorithms are LPB
uniform for the changes in spectral attributes of light and
LPBrotational invariance for the changes in affine
transformations [145].
The common texture analysis algorithms are Gray-Level
Co-occurrence matrix (GLCM), Gabor filters, LBP with
Rotation Invariance, Local Binary Patterns (LBP), LBP
with Contrast Measures, LM 3-D Textons [146], MR8-
Textons, Multi-scale Opponent Color Representation and
Multi-resolution Histogram [147]. In graphics field, there
are researches in the texture synthesis field where on the
adjacent orientation of pairs of wavelet coefficients in the
images, the statistical descriptor is based. To extract the
texture features the coefficients of a transform are used on
the original pixel values of an image by statistical
computations [148].
6.5 Texture Descriptor:
Texture feature was used for image retrieval even in
1996[149]. Texture Descriptors encoded general visual
attributes into a numerical form which can be easily used
for the computational tasks [150]. Some advance textured
descriptors are affine transform and photometric
transformation invariant features [151]. Improved affine-
invariant transform designed for the texture recognition
were used in point detection [152].
6.5.1 Wavelet Transform:
Wavelet feature performs well when the texture images
were rotated [153]. Wavelets have been used for their
compression efficiency and their locality. The various
wavelet transforms generates series of rotation and
dilations. Lowest levels of wavelet transforms are used for
the texture representation [154-156]. A comparative study
of the early works done on texture classification from
transform-based characteristics is done [157].
6.5.2 Wavelet Packet Signature Methodology:
A generalization of supported wavelets is Wavelet packets
[158]. The use of wavelet packet was initiated by Coifman
et al.[159].The wavelet packets are ortho normal [160] and
are localized in both frequency and time and thus can be
used an alternate for the Fourier (frequency based)
analysis. The methodology for the texture classification by
the help of wavelet packet representation was introduced
[154]. The minimum-distance classifier used was
Euclidean Distance. Texture Classification by Wavelet
Packets Signature Methodology is shown in Figure 8.
Figure 8: Texture Classification by Wavelet Packets Signature Methodology [151]
6.5.3 Multi-Resolution Wavelet Packet Method:
Texture signatures that were based on the multi-resolution
wavelet packet method showed potential for accurate
classification and discrimination. For texture
segmentation, the methods of scale-space analysis which
was done using this method gave efficient representation
[161].
6.5.4 Wavelet Packets and Fuzzy Spatial Relations:
In CBIR system the relevant database images with respect
to the user’s defined image are recovered using the
features like texture, color, and shape [162]. But there was
a need for more sophisticated image retrieval system for
this a method based on the wavelet packets was
introduced. The Fuzzy similarity measure was introduced
in FIRM [163] in which fuzzy features were used. Wavelet
transform provides structure for analysis and at different
scales properties of images [164].
6.5.5 Local Spatial Frequency Properties for Texture
Features:
Local spatial frequency properties can be utilized to obtain
attributes of texture. These attributes can be shared in the
wavelet area by wavelet filter for convolution in the spatial
area. For the extraction of colored texture mixing of
wavelet features was done [165]. For multi-resolution
Feature Extraction, the arbitrary tree structure was used,
analysis of multi-resolution considers the spatial
similarities across sub-bands [166].
6.5.6 Application of Texture Features:
Texture features have been used for images of documents,
[167] segmentation-based recognition, [168] and satellite
images. [169] Texture features are used in various CBIR
systems along with color, shape, geometrical structure and
sift features.
6.6 Color Co-Occurrence Matrix for Texture
Feature Extraction:
A unique method is introduced which is used to color co-
occurrence for extraction of texture feature and for
Texture Selection and
Sampling
Partition of Wavelet Packet
Space
Discrimination Using Simple
Minimum-Distance
Classifier
Discrimination Using a
Neural Network Classifier
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measuring similarity into two color images [170]. The
feature obtained gives information about the texture
correlation [171] and color information. This technique
gave better result in terms of retrieval accuracy (recall and
precision) as compare to the GLCM and histogram
technique.
Classification of various methods used for feature
extraction mostly comprises of two main categories
statistical based feature extraction and structural based
feature extraction. Statistical feature extraction methods
are a morphological operator, adjacency graph, and co-
occurrence matrices while statistical feature extraction
methods are world decomposition and Markov random
field. The categories are shown by the help of following
diagram [172-173]. Categories of Feature Extraction
Methods are given in Figure 9.
Figure 9: Categories of Feature Extraction Methods [172-173]
VII. CONCLUSION:
Large databases of images have to be searched to retrieve
query related images with the help of CBIR System. A
manual search of such image databases is inefficient and
exhaustive. Accurate retrieval of images from numerous
digital images is challenging, therefore, CBIR Systems is
an intensive research area. Semantic Gap is a deviation
from the information that is acquired from visual query
data and the already stored information in the database on
the similarities basis. For better retrieval of images from
databases, certain features are used. Feature such as shape,
color and texture are most generally used in feature
extraction phase. Growing need for effective image
retrieval has made the feature selection phase the most
critical step. This survey paper consists of the brief
introduction of features of CBIR system. Color feature is
observed at first. Different color spaces are defined in
research work related to the use of the color feature in
CBIR system. A comparison of different techniques using
color as the main feature is presented in this survey. The
shape is the basic characteristic of segmented regions of
an image. The features of shape are extracted and matched
with features already saved in database for images. Global
shape representations, as well as local shape
representations, are used forshape descriptors produce
better retrieval results. Local shape descriptors are related
to the geometric details of an image. Local shape features
may be derived from the texture properties and the color
derivatives. Texture features are used in various CBIR
systems along with color, shape, geometrical structure and
sift features. Some advance textured descriptors are affine
transform and photometric transformation invariant
features. Improved affine-invariant transform designed for
the texture recognition were used in point detection. A
common problem with texture feature extraction is that
the image with texture surfaces has to undergo numerous
scaling and re-orientation steps which are computationally
difficult tasks. Previously Pyramid Structured Wavelets,
tree-structured wavelets, multi-resolution simultaneous
autoregressive models were the multi-resolution technique
but the Gabor filters have shown better performance.
Some recent work in the field of CBIR features is also
discussed in the survey paper.
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AUTHORS’ PROFILES
Anum Masood, Lecturer,
Department of Computer
Science, CIIT, Wah
Campus, received her
Master of Computer
Science and MS (CS)
degree from COMSATS
Institute of Information Technology, WahCantt. She is
now serving as Lecturer, Department of Computer
Science, CIIT, Wah Campus. Her areas of interest are
Image Processing, Computer vision, and Medical Imaging.
Muhammad Alyas Shahid received
his Master of Computer Science
degree in 2002. He received his
MS(CS) in 2017 from COMSATS
Institute of Information Technology,
WahCantt with specialization in
Image Processing. He is into
teaching field from 1998 till date.
Currently he is serving as a Lecturer of Computer Science in
POF Institute of Technology, WahCantt. His research interests
are Image Processing, Multimedia Processing, and Computer
Networks & Security.
Muhammad Sharif, PhD,
Associate Professor COMSATS
Institute of Information
Technology, WahCantt received
his MSc in Computer Science from
Quaid-e-Azam University,
Islamabad. He received his
MS(CS) and PhD(CS) from
COMSATS Islamabad with specialization in Image Processing.
He is into teaching field from 1995 till date. His research
interests are Image Processing, Computer Networks & Security,
Parallel and Distributed Computing (Cluster Computing) and
Algorithms Design and Analysis.