Image Mining Techniques Research Articles

A Review of Image Mining Technique

A Review of Image Mining Technique

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MINING OF IMAGES USING RETRIEVAL TECHNIQUES

Today’s world is digital. Use of different social websites is become a part of our day to day life. These social websites have acquired great popularity because of their user-friendly features and their content. Nowadays internet and mobile networks are widely used everywhere and so use of images. Because of evolution in hardware as well as software it is possible to store large amount of multimedia data. In short, now a day it becomes easy to store huge amount of images by using image processing techniques. As number of images and databases are increasing day by day, there is a need for new image retrieval techniques that should be fulfilled. Image mining is derived from data mining which is a method to extract information from digital image. The purpose of this paper is to present a review of the various image mining techniques used in different applications as image retrieval, Matching, Pattern recognition etc.given by different researchers. An information or knowledge can be extracted from the image by using image mining technique. This paper proposes the survey in image retrieval techniques. Image retrieval and data mining have huge applications in the sector of image processing, pattern recognition, image matching, image mining, feature extraction, computer vision, etc.

Harmonizing freedom and protection: Adolescents' voices on automatic monitoring of social networking sites

Automatic monitoring of user-generated content on social networking sites (SNSs) aims at detecting potential harm for adolescents by means of text and image mining techniques and subsequent actions by the providers (e.g. blocking users, legal action). Evidently, current research is primarily focused on its technological development. However, involving adolescents' voices regarding the desirability of this monitoring is important; particularly because automatic monitoring might invade adolescents' privacy and freedom, and consequently evoke reactance. In this study, fourteen focus groups were conducted with adolescents (N=66) between 12 and 18years old. The goal was to obtain insights into adolescents' opinions on desirability and priorities for automatically detecting harmful content on SNSs. Opinions reflect the contention between a need for protection online versus the preservation of freedom. Most adolescents in this study are in favour of automatic monitoring for situations they perceive as uncontrollable or that they cannot solve themselves. Clear priorities for detection must be set in order to ensure the privacy and autonomy of adolescents. Moreover, monitoring actions aiming at the prevention of harm are required.

A Survey on Image Mining, its Techniques and Application

We know that today’s world is digital world and we have use digital data such as video, audio, images etc. in various fields for various purposes. In present scenario, image plays vital role in every aspect of business such as business images, satellite images, and medical images and so on. Image mining is challenging field which extends traditional data mining from structured data to unstructured data such as image data. The main aim of this paper is to present a survey of the various techniques used for image mining applications. Forests fires are a significant problem. To fight against these disasters, the accurate prediction of forest fire is a crucial issue. The increase in the number of forest fires in the last few years has forced governments to take precautions. If the fire fighters know where the fire will be in sometimes it would be easier for them to stop the fire. Therefore a big need for predicting the fire behavior exists. In this paper various techniques of image mining and different algorithms used to analyze a key event –fire is studied. This paper covers literature survey of image mining techniques and its applications.

THE IDENTIFICATION OF PILL USING FEATURE EXTRACTION IN IMAGE MINING

With the help of image mining techniques, an automatic pill identification system was investigated in this study for matching the images of the pills based on its several features like imprint, color, size and shape. Image mining is an inter-disciplinary task requiring expertise from various fields such as computer vision, image retrieval, image matching and pattern recognition. Image mining is the method in which the unusual patterns are detected so that both hidden and useful data images can only be stored in large database. It involves two different approaches for image matching. This research presents a drug identification, registration, detection and matching, Text, color and shape extraction of the image with image mining concept to identify the legal and illegal pills with more accuracy. Initially, the preprocessing process is carried out using novel interpolation algorithm. The main aim of this interpolation algorithm is to reduce the artifacts, blurring and jagged edges introduced during upsampling. Then the registration process is proposed with two modules they are, feature extraction and corner detection. In feature extraction the noisy high frequency edges are discarded and relevant high frequency edges are selected. The corner detection approach detects the high frequency pixels in the intersection points. Through the overall performance gets improved. There is a need of segregate the dataset into groups based on the query image’s size, shape, color, text, etc. That process of segregating required information is called as feature extraction. The feature extraction is done using Geometrical Gradient feature transformation. Finally, color and shape feature extraction were performed using color histogram and geometrical gradient vector. Simulation results shows that the proposed techniques provide accurate retrieval results both in terms of time and accuracy when compared to conventional approaches.

Image mining framework and techniques: a review

Image mining refers to a data mining technique where images are used as data. It supports a large field of applications like medical diagnosis, agriculture, industrial work, space research and obviously the educational field. The image mining technique can extract knowledge and exciting patterns which are not stored in the database by analysing the images using various tools. The new era of advanced technology and high storage capability supports the growth of large and detailed image database. This review paper presents a detailed view on the existing research works in the area of image mining and also summarised the different techniques used.

Image Mining: Review and New Challenges

Besides new technology, a huge volume of data in various form has been available for people. Image data represents a keystone of many research areas including medicine, forensic criminology, robotics and industrial automation, meteorology and geography as well as education. Therefore, obtaining specific in-formation from image databases has become of great importance. Images as a special category of data differ from text data as in terms of their nature so in terms of storing and retrieving. Image Mining as a research field is an interdisciplinary area combining methodologies and knowledge of many branches including data mining, computer vision, image processing, image retrieval, statis-tics, recognition, machine learning, artificial intelligence etc. This review focuses researching the current image mining approaches and techniques aiming at widening the possibilities of facial image analysis. This paper aims at reviewing the current state of the IM as well as at describing challenges and identifying directions of the future research in the field.

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Segmentation Based Image Mining Algorithms for Productivity of E-Cultivation

Image Mining techniques are suggested for agriculture for crop quality-evaluation and defect identification. A review of some of the important segmentation based algorithms and recent trends in image processing techniques which are applicable to crop quality evaluation and defect identification is presented. Image segmentation techniques are used for the detection of diseases. This demonstrates the use of the Agriculture information system, E-Cultivation process for increasing the productivity. Sample Results of some of the image segmentation algorithms used for crop disease identification and productivity measurement in E-Cultivation which are obtained using math lab are presented.

A novel image mining technique for classification of mammograms using hybrid feature selection

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and gray-level co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for gray-level co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging.

Efficient Image Mining Technique for Classification of Mammograms to Detect Breast Cancer

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class. Total of 24 features including histogram intensity features and GLCM features are extracted from mammogram images. A hybrid approach of feature selection is proposed which approximately reduces 75% of the features and new decision tree is used for classification. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns.

A Hybrid Image Mining Technique using LIMbased Data Mining Algorithm

ABSTRACT The field of image retrieval and mining has become a vibrant research area due to speedy enhancement in the volume of digital image databases. Nowadays, a large portion of information is in visual form; it is essential and certainly pleasing to search for images by content. Image mining has a variety of applications in various sectors like medical diagnosis, biology, remote sensing, space research, etc. This research is to determine the exact images while mining an image (multimedia) database and proposes a novel approach for mining images using LIM based image matching technique with neural networks. This process is independent of too many parameter setting to generate a robust solution. It is designed and implemented on MATLAB and is tested with the images of various databases. Appropriate measures were devised to evaluate the performance of the system. The performances of the LIM based image matching technique results were noteworthy and comparable. While comparing with the number of false retrievals with the correct retrievals, the anticipated system performance level will be suited for several simple day to day multimedia database applications and image mining systems. The image mining system derived from the LIM based image matching technique provided promising results.

The role of bioinformatics in two-dimensional gel electrophoresis.

Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.

A computer aided consultant system for mammogram diagnosis

A computer-aided consultant system for mammogram diagnosis is proposed in this paper based on mammogram segmentation as an image mining technique, to aid radiologistis in X-ray film interpretation. The general architecture of the system is introduced first, followed by a discussion of mammogram segmentation using logic filter, an analysis of the statistical data to the diagnostics with respect to different clinical information, and a brief introduction to a fuzzy decision making subsystem. Finally some experimental results are given.