This document discusses image mining techniques for image classification and feature extraction. It begins with an overview of the image mining process, including image pre-processing, feature extraction, image mining (classification and clustering), and interpretation/evaluation. It then reviews several related works on image mining and discusses research gaps. Finally, it outlines some applications of image mining such as medical imaging and satellite imagery analysis.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
Content Based Image Retrieval: A ReviewIRJET Journal
This document reviews content-based image retrieval (CBIR) techniques. It discusses how CBIR systems extract features like color, texture, and shape from images to enable search and retrieval of similar images from a database. Color features may use color histograms in color spaces like RGB. Texture features can use techniques like Gabor wavelet transforms and Tamura features. Shape is often extracted using edge detection methods. The document outlines the general CBIR workflow of feature extraction, matching, and retrieval. It also reviews several existing CBIR methods and techniques used for feature extraction.
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.
IRJET - An Enhanced Approach for Extraction of Text from an Image using Fuzzy...IRJET Journal
This document presents an approach for extracting text from images using fuzzy logic. It involves preprocessing the image to remove noise, segmenting the image to extract individual characters, and then using fuzzy logic to identify the characters by comparing segmented characters to trained data and determining the degree of matching. The key steps are pre-processing, segmentation, feature extraction using techniques like statistical and geometrical features, classification using a convolutional neural network, and then using fuzzy logic to accurately identify characters by finding the highest matching value between segmented and trained characters. The goal is to recognize and extract text from the image in an editable format.
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
This document discusses content-based image mining techniques for image retrieval. It provides an overview of image mining, describing how image mining goes beyond content-based image retrieval by aiming to discover significant patterns in large image collections according to user queries. The document reviews several existing image mining techniques, including those using color histograms, texture analysis, clustering algorithms like k-means, and association rule mining. It discusses challenges in developing universal image retrieval methods and proposes combining low-level visual features with high-level semantic features. Overall, the document surveys the state of the art in content-based image mining and retrieval.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
Content Based Image Retrieval: A ReviewIRJET Journal
This document reviews content-based image retrieval (CBIR) techniques. It discusses how CBIR systems extract features like color, texture, and shape from images to enable search and retrieval of similar images from a database. Color features may use color histograms in color spaces like RGB. Texture features can use techniques like Gabor wavelet transforms and Tamura features. Shape is often extracted using edge detection methods. The document outlines the general CBIR workflow of feature extraction, matching, and retrieval. It also reviews several existing CBIR methods and techniques used for feature extraction.
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.
IRJET - An Enhanced Approach for Extraction of Text from an Image using Fuzzy...IRJET Journal
This document presents an approach for extracting text from images using fuzzy logic. It involves preprocessing the image to remove noise, segmenting the image to extract individual characters, and then using fuzzy logic to identify the characters by comparing segmented characters to trained data and determining the degree of matching. The key steps are pre-processing, segmentation, feature extraction using techniques like statistical and geometrical features, classification using a convolutional neural network, and then using fuzzy logic to accurately identify characters by finding the highest matching value between segmented and trained characters. The goal is to recognize and extract text from the image in an editable format.
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
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.
A Survey of Image Processing and Identification Techniquesvivatechijri
Image processing is always an interesting field as it gives enhanced visual data for human
simplification and processing of image data for transmission and illustration for machine preception. Digital
images are processed to give better solution using image processing. Techniques such as Gray scale
conversion, Image segmentation, Edge detection, Feature Extraction, Classification are used in image
processing.
In this paper studies of different image processing techniques and its methods has been conducted.
Image segmentation is the initial step in many image processing functions like Pattern recognition and image
analysis which convert an image into binary form and divide it into different regions. The technique used for
segmentation is Otsu’s method, K-means Clustering etc. For feature extraction feature vector in visual image is
texture, shape and color. Edge detector with morphological operator enhances the clarity of image and noise
free images. This paper also gives information about algorithm like Artificial Neural Network and Support
Vector Mechanism used for image classification. The image is categorized into the receptive class by an ANN
and SVM is used to compile all the categorized result. Overall the paper gives detail knowledge about the
techniques used for image processing and identification.
A Survey on Techniques Used for Content Based Image Retrieval IRJET Journal
This document reviews various content-based image retrieval techniques that use different feature extraction methods. It discusses techniques that use color and texture features, color and shape features, relevance feedback with support vector machines and feature selection, combining color, texture and shape features, and using multiple support vector machine ensembles. Each technique is summarized in terms of advantages and disadvantages. In general, using multiple features and support vector machines can improve retrieval accuracy but may also increase computational complexity. Combining features may retrieve semantically similar images but be time consuming. The document concludes that using support vector machine ensembles can narrow the search space for large databases while achieving good retrieval performance.
IRJET- Retrieval of Images & Text using Data Mining TechniquesIRJET Journal
This document discusses using data mining techniques like clustering and association rule mining for image retrieval. It proposes a system that extracts both visual features (e.g. color, texture) and textual features from images. The features are clustered separately, then association rules are mined by fusing the clusters. Strong association rules are selected as training data. A query image's features are mined to find matching rules to retrieve semantically related images from the database. This combines content-based and text-based retrieval to address limitations of each approach individually.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IRJET- Analysis of Plant Diseases using Image Processing MethodIRJET Journal
This document describes a method for detecting plant diseases using image processing techniques. The method involves capturing images of plant leaves using a digital camera, preprocessing the images by converting them to grayscale and removing noise. Edge detection algorithms like Canny and Sobel are then applied to detect edges. K-means clustering is used for image segmentation to segment unhealthy parts of leaves. The process results in an effective solution for segmenting diseased areas of leaves.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR has advantages over text-based retrieval but challenges remain around the semantic gap between low-level features and high-level concepts. The document also discusses evaluating retrieval performance and promising future research directions like reducing the semantic gap.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR techniques are improving but challenges remain around reducing the semantic gap between low-level features and high-level concepts. Future areas of research include developing techniques more aligned with human perception and improving efficiency and interfaces.
A Review Paper On Image Forgery Detection In Image ProcessingJennifer Daniel
This document provides a review of techniques for detecting image forgeries in image processing. It begins with an introduction to digital images and image processing. It then reviews several papers that have proposed various techniques for image forgery detection including pixel-based detection, key point-based detection, and detection of copy-move forgeries. The document also describes challenges in digital image processing and different categories of image forgery detection techniques. It concludes that accurate methods are needed to detect image forgeries using image processing approaches and reviews can help improve existing techniques.
IRJET- A Survey on Different Image Retrieval TechniquesIRJET Journal
This document discusses different techniques for content-based image retrieval. It begins by describing content-based image retrieval (CBIR) and how it uses visual features like color, texture, and shape to search for images, unlike text-based retrieval which relies on metadata. It then discusses various CBIR techniques in detail, focusing on block truncation coding (BTC) techniques. Specifically, it examines dot diffusion block truncation coding (DDBTC), which extracts color histogram and bit pattern features to retrieve images. Performance is measured using average precision and recall rates.
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.
IRJET- Image Segmentation Techniques: A ReviewIRJET Journal
1. The document discusses and reviews various techniques for image segmentation, including edge detection, threshold-based, region-based, and neural network-based methods.
2. Edge detection separates images by detecting changes in pixel intensity or color to find edges and boundaries. Threshold-based methods segment images based on pixel intensity levels compared to a threshold. Region-based methods partition images into homogeneous regions of connected pixels. Neural network-based methods can perform automated segmentation through supervised or unsupervised machine learning.
3. Prior research has evaluated these techniques, finding that edge detection works best with clear edges but struggles with noise or smooth boundaries, and thresholding methods can miss details but are simple to implement. Region-based and neural network
This document summarizes a research paper that proposes an algorithm for detecting brain tumors in MRI images based on analyzing bilateral symmetry. The algorithm first performs preprocessing like smoothing and contrast enhancement. It then identifies the bilateral symmetry axis of the brain. Next, it segments the image into symmetric regions, enhancing asymmetric edges that may indicate a tumor. Experiments showed the algorithm can automatically detect tumor positions and boundaries. The algorithm leverages the fact that brain MRI of a healthy person is nearly bilaterally symmetric, while a tumor disrupts this symmetry.
IRJET - Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...IRJET Journal
This document describes a computer-assisted method for detecting and counting four types of blood cancer (ALL, AML, CLL, CML) from microscopic blood images. The method first segments the image to identify white blood cells, then extracts lymphocytes. Shape and color features of the lymphocytes are used to classify them as normal or blast cells using SVM. The system was found to be more accurate and fast compared to manual identification methods. It aims to automatically diagnose blood cancers from images in a time-efficient and accurate manner.
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
This document summarizes research on using spatial features for content-based image retrieval (CBIR). It first discusses common CBIR techniques like feature extraction, selection, and similarity measurement. It then reviews several related works that extract spatial features like edge histograms and color difference histograms. Experimental results show integrating spatial information through image partitioning can improve semantic concept detection performance. While finer partitions carry more spatial data, coarser partitions like 2x2 are preferred to avoid feature mismatch. Future work may explore combining multiple feature domains and contexts to further enhance retrieval accuracy and effectiveness for large-scale image datasets.
This document summarizes various techniques for image segmentation that have been studied and proposed in previous research. It discusses edge-based, threshold-based, region-based, clustering-based, and other common segmentation methods. It also reviews applications of segmentation in medical imaging, plant disease detection, and other fields. While no single technique can segment all images perfectly, hybrid and adaptive methods combining multiple approaches may provide better results. Overall, image segmentation remains an important but challenging task in digital image processing and computer vision.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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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.
A Survey of Image Processing and Identification Techniquesvivatechijri
Image processing is always an interesting field as it gives enhanced visual data for human
simplification and processing of image data for transmission and illustration for machine preception. Digital
images are processed to give better solution using image processing. Techniques such as Gray scale
conversion, Image segmentation, Edge detection, Feature Extraction, Classification are used in image
processing.
In this paper studies of different image processing techniques and its methods has been conducted.
Image segmentation is the initial step in many image processing functions like Pattern recognition and image
analysis which convert an image into binary form and divide it into different regions. The technique used for
segmentation is Otsu’s method, K-means Clustering etc. For feature extraction feature vector in visual image is
texture, shape and color. Edge detector with morphological operator enhances the clarity of image and noise
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This document discusses using data mining techniques like clustering and association rule mining for image retrieval. It proposes a system that extracts both visual features (e.g. color, texture) and textual features from images. The features are clustered separately, then association rules are mined by fusing the clusters. Strong association rules are selected as training data. A query image's features are mined to find matching rules to retrieve semantically related images from the database. This combines content-based and text-based retrieval to address limitations of each approach individually.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IRJET- Analysis of Plant Diseases using Image Processing MethodIRJET Journal
This document describes a method for detecting plant diseases using image processing techniques. The method involves capturing images of plant leaves using a digital camera, preprocessing the images by converting them to grayscale and removing noise. Edge detection algorithms like Canny and Sobel are then applied to detect edges. K-means clustering is used for image segmentation to segment unhealthy parts of leaves. The process results in an effective solution for segmenting diseased areas of leaves.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR has advantages over text-based retrieval but challenges remain around the semantic gap between low-level features and high-level concepts. The document also discusses evaluating retrieval performance and promising future research directions like reducing the semantic gap.
This document discusses various techniques for image retrieval, including text-based, content-based, and hybrid approaches. Content-based image retrieval (CBIR) extracts visual features like color, texture, shape from images and is able to retrieve similar images to a query image. CBIR systems segment images, extract features, search databases, and return results. CBIR techniques are improving but challenges remain around reducing the semantic gap between low-level features and high-level concepts. Future areas of research include developing techniques more aligned with human perception and improving efficiency and interfaces.
A Review Paper On Image Forgery Detection In Image ProcessingJennifer Daniel
This document provides a review of techniques for detecting image forgeries in image processing. It begins with an introduction to digital images and image processing. It then reviews several papers that have proposed various techniques for image forgery detection including pixel-based detection, key point-based detection, and detection of copy-move forgeries. The document also describes challenges in digital image processing and different categories of image forgery detection techniques. It concludes that accurate methods are needed to detect image forgeries using image processing approaches and reviews can help improve existing techniques.
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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.
IRJET- Image Segmentation Techniques: A ReviewIRJET Journal
1. The document discusses and reviews various techniques for image segmentation, including edge detection, threshold-based, region-based, and neural network-based methods.
2. Edge detection separates images by detecting changes in pixel intensity or color to find edges and boundaries. Threshold-based methods segment images based on pixel intensity levels compared to a threshold. Region-based methods partition images into homogeneous regions of connected pixels. Neural network-based methods can perform automated segmentation through supervised or unsupervised machine learning.
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This document summarizes a research paper that proposes an algorithm for detecting brain tumors in MRI images based on analyzing bilateral symmetry. The algorithm first performs preprocessing like smoothing and contrast enhancement. It then identifies the bilateral symmetry axis of the brain. Next, it segments the image into symmetric regions, enhancing asymmetric edges that may indicate a tumor. Experiments showed the algorithm can automatically detect tumor positions and boundaries. The algorithm leverages the fact that brain MRI of a healthy person is nearly bilaterally symmetric, while a tumor disrupts this symmetry.
IRJET - Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...IRJET Journal
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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
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A Survey Paper on Image Classification and Methods of Image Mining
Article in International Journal of Computer Applications · July 2017
DOI: 10.5120/ijca2017914765
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2. International Journal of Computer Applications (0975 – 8887)
Volume 169 – No.6, July 2017
10
A Survey Paper on Image Classification and
Methods of Image Mining
Sandeep Pandey
Department of Computer Science and Engineering
MANIT, Bhopal, M.P., INDIA
Sri Khetwat Saritha, PhD
Department of Computer Science and Engineering
MANIT Bhopal, M.P., INDIA
ABSTRACT
Image mining that consists of Image processing, Databases,
Data mining, Machine learning, and artificial Intelligence,
focuses on extraction of patterns from large collection of
images. Although many researches have been done on many
of these areas but issues are still there in image mining
techniques. For instance, data mining techniques can’t
automate mining of images from large set of images. In this
paper, a general approach is been discussed to mine data
based on many researches done till now. It will help readers
who want to know about existing image mining techniques,
and knowledge mining from large image data set to progress
in this area.
General Terms
Data mining, image processing, neural network, machine
learning.
Keywords
Image mining, image classification, image clustering, image
pre-processing, content based image retrieval (CBIR).
1. INTRODUCTION
Image mining is an interdisciplinary field that is based on
Image processing, data mining, machine learning, databases
and artificial intelligence. As the world has grown and social
networking is growing dramatically, there is a need of
extracting meaningful images out of the pool of network. The
emphasis of image processing is on the understanding of
certain characteristics of specific image and the focus of data
mining is to apply algorithms to mine data (images) more
accurately and efficiently. On the other hand, the prominence
of machine learning is to make network learn about
patterns/features so that errors in output can be minimized and
to classify images using specific features selected during
image processing. This paper briefly explains image mining
process proceeded by literature reviews based on different
papers, research gaps found, application of image mining
techniques and lastly conclusion.
2. IMAGE MINING PROCESS
Image mining can be expressed as a sum of four essential
phases (Figure 1), described as follows:
2.1 Image Pre-Processing
It is necessary to improve quality of image before using it to
extract features form it. Images are pre-processed to create
high quality images that result in more transparent
categorization. Pre-processing of an image is also important
for removing noise from the images that may cause unwanted
results. There are linear and non-linear image filters to remove
noise in the image. In pre-processing, we can do Image
thresholding [1], edge detection [1], border tracing [1],
wavelet based segmentation [2]. We may also use low pass
filters, high pass and band pass filters [1] for removing noise
from the images.
2.2 Feature Extraction
Features of an image play an important role in distinguishing
and categorizing set of images in their respective categories.
There are many feature like color, edge, texture, shape, and
boundary that can be extracted from the image. There are
various method used for feature extraction described next.
2.2.1 Image Thresholding
It is the simplest method of image segmentation. Extreme
contrast stretching results thresholding. From a gray-scale
image, thresholding can be used to create binary images. In
simple way, it is a process of replacing all image pixels Ii,j
(indicates intensity values of pixel located in ith
row and jth
column), whose intensity is less than a fixed or constant
intensity, called thresholding of an image.
2.2.2 Edge detection
Variation of features of scene like brightness, gives rise to
edges. These are representations of discontinuities of intensity
function of an image. The purpose of detecting sharp
discontinuities in image brightness is to capture important and
useful changes in properties of the world. It can be shown that
under general assumptions for an image, discontinuities in
image brightness corresponds to discontinuity in depth,
discontinuities in surface orientation, change in material
properties, and variations in scene illumination. There are
various methods to detect these discontinuities as edges. All
these methods are based on derivatives and can be classified
into two major categories listed next.
2.2.2.1 First Order Method
Prewitts and Sobel operators [1] are some of the tool used for
edge detection. These operators are 3X3 dimension masks that
is to be applied on whole image to get edges.
2.2.2.2 Second order method
Laplacian operator [1] is used to get sharp edges in second
derivative method. These are also called isotropic filters. It is
very sensitive to noise. If an image contains noise, Laplacian
will ruin the entire image.
2.2.3 Boundary tracing
It is also known as contour tracing of a binary digital region
can be thought of as a segmentation technique that identifies
the boundary pixels of a digital image. This is done tracing
whole image pixels top to down, left to right and the result
can be encoded in Run-Length [1] code.
2.2.4 Image color extraction
It can be used to extract color as a feature of the image. Color
histogram feature in image is used to represent color
distribution of the image. The red, green, blue distribution in
an image can be controlled by using histogram equalization.
In the process of equalization of an image, all red, green, blue
pixels of that image are calculated, and redistributed to make
image contrast balanced globally in the image. This allows the
area of lower contrast to gain higher contrast.
3. International Journal of Computer Applications (0975 – 8887)
Volume 169 – No.6, July 2017
11
Figure 1: Image Mining Process [3]
2.2.5 Image texture extraction
Image texture gives us information about the spatial
arrangement of color or intensities in an image or selected
region of an image. It is set of metrics calculated in image
processing which is used to quantify the texture of the image.
Image textures are manually created or found in natural space.
2.3 Image mining
Mining useful knowledge from large set of image database is
the main objective of image mining. Image mining is used in
various forms based on queries (see Table 1) similarity search
techniques (see Table 2) and type of learning such as
supervised and unsupervised learning.
2.3.1 Image classification [2]
The intent of classification process is to categorize all pixels
in a digital image into several classes. It is a supervised
learning method used to classify images based on some pre-
known results. The main task here is to assign an input image
one label from a fixed set of categories. All classification
algorithms are based on the assumption that the image in
question depicts one or more features and that each of these
features belongs to one of several distinct and exclusive
classes. Various classifier used for this purpose are- Bays
classifier, Neural Networks (MLP, RBF, SVM and many
more), Decision Tree classifier, Genetic Algorithms.
Classification of image is done in mainly two phases, learning
phase and test phase. In learning phase, images taken are
different and learning is made on the basis of output class. In
the testing phase, image features/specification are used to map
image to an output class. One of the most important method in
classification of image is decision tree. Based on whole
sample, decision trees divide decision space to smaller areas
as a return. Decision tree breakdown the complex problems
into smaller problem based on some decision taken while
taking action.
2.3.2 Image clustering [4]
It is an automated unsupervised method in which samples are
divided in various groups based on some similar
characteristics. These groups individually are called cluster.
This means, cluster is a collection of objects, where object
share some property/feature with some object in the cluster.
There are various methods of clustering data in groups. Some
of them are Partitioning methods, hierarchical methods, and
Grid-based methods. Clustering don’t require output feature
vector like classification do, rather they continuously add new
sample to a group based on their property that closely matches
to any sample of the group and in this ways growing the size
of cluster by adding more samples as they arrive.
Table 1: Queries in Content Based Image Retrieval
System
Image Sample Based
Queries
Image Feature Specification
Queries
This compares the given
image to all the images in
database.
This compares feature of
image with pre-collected
features of images.
This process is slightly
slower because it inputs
whole image for comparison.
This process is slightly fast as
it inputs only the feature
vector of image.
Table 2: Similarity Search in Image Data
Description-Based
Retrieval Systems
Content-Based Retrieval
Systems
This performs object
retrieval based on image
descriptions such as
keywords, captions, size,
and time of creation.
This performs object
retrieval based on content of
the image rather than
metadata of the image.
This process is labor
intensive if performed
manually.
This process is automated as
features of the images are
extracted and used for
retrieval.
Result of this process is
poor if automated.
Results are not affected.
4. International Journal of Computer Applications (0975 – 8887)
Volume 169 – No.6, July 2017
12
2.4 Interpretation and Evaluation
Evaluation of retrieval is important in image mining process.
Many different methods for measuring the performance of the
system have been proposed by researchers. But the most
common method for evaluation is Precision and Recall. It is
usually presented as precision vs recall graph. The formulae
used for evaluating the value for precision and recall are:
Precision=
Recall=
3. LITRATURE REVIEW
[5] Concerns the extraction of implicit knowledge, image data
relationship, or other patterns not explicitly stored in the
images. This paper starts with data pre-processing which
contains image thresholding, edge detection and border
tracing. Extraction of multi-dimensional feature vector such as
color, edge, texture is discussed. And finally, learning
methodologies (supervised learning and unsupervised
learning) followed by association rule mining discussed.
[2] Developed image clustering and categorization technics by
using concept of text mining. Applied FUZZY technics on
Images and meaningful content. In this approach, user has to
first upload the image and give brief description to each
image. Then, pre-processing of image starts in which sentence
is separated, and divided into verb argument structure. Cluster
of the images is created based on frequency of common
features among images.
[6] Proposed a novel unsupervised method for the image
classification based on various feature distribution of textual
images. In first level of classification, image is converted into
grayscale image then histogram feature like mean, variance,
and skewness are extracted. Using weka-J48, decision tree
classifier, images are classified as DOC and NON-DOC
images. In second level of classification, grayscale image is
sliced in binary form, from that GLCM (grey level coherence
matrix) is formed. Then, energy and contrast value of image is
calculated. Lastly, depending on these values, decision is
taken about image that it is captioned image or scene text
image.
[4] Proposed effective clustering to increase speed of image
retrieval system. Fuzzy C-means clustering is used to cluster
images. Pre-clustering is based on RED group, GREEN
group, and BLUE group of images. Entropy is used to
compare images with same threshold constraints.
[7] Break the image object into meaningful components such
as color, texture, shape, etc. and querying the image objects
after representing them to retrieve the discovered knowledge
3.1 RESEARCH GAP
Improvements on image pre-processing
technologies, including feature extraction [5].
Devise highly efficient and extensible image mining
algorithms [5].
Introduce domain knowledge into image mining,
which are essential for understanding mining results
[5].
Images are not classified using their own feature
instead user interpretation of image is used for
clustering images [6].
Further research should focus on extending the
features of image to distinguish images more
accurately [8].
Combination of scene and caption text images, what
would be result? [2]
This application can be used in future to classify the
medical images in order to diagnose the right
disease verified earlier [4].
Chosen only most frequent values of some features
in order to reduce number of dimensions, which
may be the reason of inefficiency [7].
4. APPLICATION OF IMAGE MINING
Mining on medical images is to acquire valuable knowledge
and modes, which can be used for discovering abnormal
situations not consistent with previous modes. Mining can be
used to classify CT scan brain images into three categories
namely normal, benign, and malign. Image mining can also be
used on satellite cloud imagery. Neural network can be used
to do image clustering on various image collections. It can
assist physicians for efficient classification with multiple
keywords per image to improve accuracy.
5. CONCLUSION
Image mining is not the newest topic but more work can be
done in this area because image objects are hard to define and
no best algorithm is proposed till now. Every methodology
proposed have issue related to accuracy (input versus output
image) or speed of processing. This paper covered various
strategies used previously and research gap related to those
strategies. These research gaps can be used for implementing
a new image mining technique.
6. REFERENCES
[1] R. E. W. Rafael C. Gonzalez, DIGITAL IMAGE
PROCESSING, PEARSON, 2008.
[2] M. C. D. P. Mr. Dipak R. Pardhi, "Image Classification
Using Text Mining and Feature Clustering (Text
Document and Image Categorization Using Fuzzy
Similarity Based Feature Clustering)," IJETTCS, pp.
136-141, 2015.
[3] K. K. Prabhjeet Kaur, "Review of Different Existing
Image Mining Techniques," IJARCSSE, vol. 4, no. 6, pp.
518-524, 2014.
[4] D. D. A.Kannan, "Image Clustering and Retrieval using
Image Mining Techniques," in IEEE, TAMILNADU,
2010.
[5] Y. S. HU MIN, "OVERVIEW OF IMAGE MINING
RESEARCH," in The 5th International Conference on
computer science and education, hefei, china, 2010.
[6] M. J. M. A. M. K. M. B. Prof.Mrs.Sushma Nandgaonkar,
"Image Mining of Textual Images Using Low-Level
Image Features," maharastra, IEEE, 2010, pp. 588-592.
[7] A. K. A. S. Imran Khan, "Object Analysis in Image
Mining," in INDIAcom, NEW DELHI, 2015.
[8] A. W. Neethu Joseph.c, "Retrieval of images using data
mining techniques," KERALA, IEEE, 2014, pp. 204-208.
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