A STUDY OF COLOR, TEXTURE FEATURE OF IMAGE RETRIEVAL IN IMAGE MINING APPLICATIONS
Hlaing Htake Khaung Tin
Faculty of Information Science
University of Computer Studies, Yangon, Myanmar hlainghtakekhaungtin@gmail.com Abstract: Image mining is the process of searching and discovering valuable information and knowledge in large volumes of data. Some of the methods used to gather knowledge are, Image Retrieval, Data Mining, Image Processing and Artificial Intelligence. Image retrieval system is used in a wide range of applications such as geography, medical, architecture, advertising, design, military and albums. Image mining is an extended field of image and data mining that is concerned with the process of knowledge discovery concerning digital
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The knowledge retrieved can be used by individual or organization for various purposes to make predictions and profitable output. [1]
II. LITERATURE REVIEW
In this section, we discuss recent literature on some key aspects of image mining and image retrieval. Image mining deals with the extraction of image patterns from a large collection of images. Clearly, image mining is different from low-level computer vision and image processing techniques because the focus of image mining is in extraction of patterns from large collection of images, whereas the focus of computer vision and image processing techniques is in understanding and/or extracting specific features from a single image. While there seems to be some overlaps between image mining and content-based retrieval (both are dealing with large collection of images), image mining goes beyond the problem of retrieving relevant images. In image mining, the goal is the discovery of image patterns that are significant in a given collection of images [4].
Image mining is an interdisciplinary endeavour that draws upon expertise in various fields like computer vision, image retrieval, matching and pattern
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Second method mines a combination of associated alphanumeric data and collection of images. Research in Image mining can be broadly classified in two main directions (1) Domain specific applications (2) General applications. Both are used to extract most relevant image feature and later to generate image patterns. A vast amount of image data is generated in daily life and in various fields like medical, astronomy, sports and all kinds of photographic images. It is still at the experimental stage and growing field of research. Lack of understanding in the research issues of image mining is the obstacle to rapid progress
As part of this initiative, the company will establish an online knowledge base accessible via the company intranet and hard-copy memory aids and reference in decision-making and conflict resolution.
In this paper, we propose a new content-based image retrieval technique using Zernike chromaticity distribution momentsand rotation-scale invariant Contourlet texture feature, which achieves higher retrieval efficiency. The rest of this paper is organized as follows. Section 2 presents Zernike chromaticity distribution color moments extraction. Section 3
Prediction is a natural way to make sense of the world by asking questions. The human brain predicts and organizes information constructed by experience, not instruction, constantly. Everything we know about the world is a summary of our knowledge.
Data mining is process of computing the data from the large data sets involving methods on to intersection of statistics, machine learning,
Data mining is the technique to interpret the data from other perspective and summarize the data so that the data can be useful information. Technically, data mining is a process to identify relations or patterns in the databases to predict the likelihood of future events. According to Eliason et al, there are three systems for healthcare organization to implement the mining data systems. The three systems are the analytics system, the content system and the deployment system. The analytics system is a system that used to collect all data such as patients clinical data, patients financial data, patients satisfactory data and other data. The content system is used to store all medical evidenced data. The deployment system is used to make new organization structure. There are several elements that consist in data mining which are first extract, transform and load transaction data onto the data warehouse system, second, store and manage the data in a multidimensional system, third, provide data access to information technology professionals, forth, analyze the data by application software and lastly, present the data in graph or table format.
This does not only allow them to make meaning of the available information but at the same time enhances their ability to make the best use of the information that they have gathered so far.
Many do not consider where images they see daily come from. A person can see thousands of different designs in their daily lives; these designs vary on where they are placed. A design on a shirt, an image on a billboard, or even the cover of a magazine all share something in common with one another. These items all had once been on the computer screen or on a piece of paper, designed by an artist known as a graphic designer. Graphic design is a steadily growing occupation in this day as the media has a need for original and creative designs on things like packaging or the covers of magazines. This occupation has grown over the years but still shares the basic components it once started with. Despite these tremendous amounts of growth,
Predictive Modeling: Utilization of the patterns discovered in the discovery step to forecast possible future conducts or behavior.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
Birchler and Butler (2007) stated that there are many reasons to know in depth about economics of information, which are information is an interesting economic good, economics is about information, information is of strategic importance and information economics is a young field with practical relevance in many context.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
2. Face recognition: Face recognition is based on both the shape and location of the eyes, eyebrows, nose, lips and chin. It is non intrusive method and very popular also. Facial recognition is carried out in two ways ...
In the world today, information is an important aspect in almost every part of our life. From what time the movie we want to see begins to whether we should buy stock in Dell or IBM, we depend on accurate information. Is this kind of information a commodity? The dictionary defines a commodity as something valuable or useful (Webster 1993). Presently, information is a commodity because people are willing to pay high prices for information in order to make better decisions. In this paper, I will give many examples of how information acts as a commodity. I will also show how information acts as a commodity in other areas than just technology and business.
Decision Making Insights. I will be most successful when I have all the information necessary for decision-making, as my dominant cognitive styles are planning and knowing. This approach does not leave room for ambiguity and stifles flexibility and creativity. Consequently, I thrive in established organizations that value hierarchy, procedures, and open communication, because they assist my natural cognitive processes. (Pearson Education, 2016)