Mining Images for Structure

2011 ◽  
pp. 805-809
Author(s):  
Terry Caelli
Keyword(s):  
Data Mining ◽  
Image Data ◽  
Multimedia Retrieval ◽  
Image Feature ◽  
Perceptual Information ◽  
Sporting Events ◽  
Image Content ◽  
Symbolic Form ◽  
Color Histograms ◽  
Per Se

Most data warehousing and mining involves storing and retrieving data either in numerical or symbolic form, varying from tables of numbers to text. However, when it comes to everyday images, sounds, and music, the problem turns out to be far more complex. The major problem with image data mining is not so much image storage, per se, but rather how to automatically index, extract, and retrieve image content (content-based retrieval [CBR]). Most current image data-mining technologies encode image content by means of image feature statistics such as color histograms, edge, texture, or shape densities. Two well- known examples of CBR are IBM’s QBIC system used in the State Heritage Museum and PICASSO (Corridoni, Del Bimbo & Pala, 1999) used for the retrieval of paintings. More recently, there have been some developments in indexing and retrieving images based on the semantics, particularly in the context of multimedia, where, typically, there is a need to index voice and video (semantic-based retrieval [SBR]). Recent examples include the study by Lay and Guan (2004) on artistry-based retrieval of artworks and that of Benitez and Chang (2002) on combining semantic and perceptual information in multimedia retrieval for sporting events.

scholarly journals Model of Multilevel Sub-Image to Find the Position of Region of Interest

2016 ◽  
Vol 3 (2) ◽  
pp. 189-196
Author(s):  
Budi Hartono ◽  
Veronica Lusiana
Keyword(s):  
Euclidean Distance ◽  
Region Of Interest ◽  
Image Data ◽  
Image Feature ◽  
Experimental Result ◽  
Main Attention ◽  
Image Content ◽  
Query Image ◽  
Image Object

Searching image is based on the image content, which is often called with searching of image object. If the image data has similarity object with query image then it is expected the searching process can recognize it. The position of the image object that contains an object, which is similar to the query image, is possible can be found at any positionon image data so that will become main attention or the region of interest (ROI). This image object can has different wide image, which is wider or smaller than the object on the query image. This research uses two kinds of image data sizes that are in size of 512X512 and in size of 256X256 pixels.Through experimental result is obtained that preparing model of multilevel sub-image and resize that has same size with query image that is in size of 128X128 pixels can help to find ROI position on image data. In order to find the image data that is similar to the query image then it is done by calculating Euclidean distance between query image feature and image data feature.

scholarly journals Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hong Huang ◽  
Risheng Deng
Keyword(s):  
Data Mining ◽  
Feature Recognition ◽  
Image Data ◽  
Technical Analysis ◽  
Machine Learning Algorithms ◽  
Technical Training ◽  
Image Feature ◽  
Mining Technology ◽  
Movement Characteristics ◽  
Real Time Tracking

Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. Moreover, this paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. In addition, this paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself. The research results show that the model constructed in this paper has certain practical effects and can be applied to actual competitions.

Research on Application of Ultrasound Medical Imaging Technology in Big Data Mining of Regional Medical Imaging

2021 ◽  
Vol 11 (3) ◽  
pp. 930-937
Author(s):  
Yubo Xie
Keyword(s):  
Data Mining ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Image Data ◽  
Image Feature ◽  
Big Data Mining ◽  
Regional Medical ◽  
Mining Methods ◽  
Medical Image Data

Ultrasound medical imaging technology is one of the main methods of medical non-invasive diagnosis, and it is the focus of research in the medical field at home and abroad. Medical images have a large amount of data and contain a wealth of image feature information and rules, which need to be studied and understood. Therefore, the research of data mining technique for reading medical images has become a very important field in the interdisciplinary research of medical and computer science. The high resolution of medical images, the mass of data, and the complexity of image feature expressions make the research of data mining technology in medical images of great academic value and broad application prospects. At present, research on data mining for medical images has just started, and there are still many problems in the direct application of existing data mining methods. Researching and exploring the theoretical and practical problems of medical image data mining, such as data mining methods and algorithms suitable for medical image, which has significant and crucial value, and it is of great importance to help physicians in clinical diagnosis of medical images. This article introduces the background, definition and basic process of data mining technology, the characteristics of medical imaging data and the key techniques of medical image data mining. In view of the data mining research of human abdominal medical images is a completely new field, human abdominal imaging is the most complicated part of medical images. Solving the problem of abdominal imaging is of great value to the entire medical image. For regional medical image big data mining, we can use ultrasound images of the human abdomen. The clustering feature extraction algorithm and its implementation based on the approximate density structure of medical images proposed in this article, and innovative research results such as classification rule mining methods, are used to mine medical image data research, automatic diagnosis of clinical medical images, and early diagnosis of clinical medicine are of great significance.

Algorithms for Data Mining

2008 ◽  
pp. 1301-1319
Author(s):  
Tadao Takaoka ◽  
Nigel K.L. Pope ◽  
Kevin E. Voges
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Image Data ◽  
Regression Trees ◽  
Clustering Techniques ◽  
Data Mining Algorithms ◽  
Mining Algorithms

In this chapter, we present an overview of some common data mining algorithms. Two techniques are considered in detail. The first is association rules, a fundamental approach that is one of the oldest and most widely used techniques in data mining. It is used, for example, in supermarket basket analysis to identify relationships between purchased items. The second is the maximum sub-array problem, which is an emerging area that is yet to produce a textbook description. This area is becoming important as a new tool for data mining, particularly in the analysis of image data. For both of these techniques, algorithms are presented in pseudo-code to demonstrate the logic of the approaches. We also briefly consider decision and regression trees and clustering techniques.

On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search

2010 ◽  
Vol 4 (4) ◽  
pp. 18-36 ◽  
Author(s):  
Jane You ◽  
Qin Li ◽  
Jinghua Wang
Keyword(s):  
Image Retrieval ◽  
Data Warehouse ◽  
Image Data ◽  
Image Feature ◽  
Image Indexing ◽  
Content Based Image Retrieval ◽  
New Approach ◽  
Guided Search ◽  
Dynamic Image ◽  
Face Features

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.

R*-Tree Based Similarity and Clustering Analysis for Images

2011 ◽  
pp. 50-61
Author(s):  
Jiaxiong Pi ◽  
Yong Shi ◽  
Zhengxin Chen
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Image Data ◽  
Multimedia Retrieval ◽  
Spatial Proximity ◽  
Similarity Analysis ◽  
Analysis Tool ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods ◽  
And Cluster Analysis

Image content analysis plays an important role for adaptive multimedia retrieval. In this chapter, the authors present their work on using a useful spatial data structure, R*-tree, for similarity analysis and cluster analysis of image contents. First, they describe an R*-tree based similarity analysis tool for similarity retrieval of images. They then move on to discuss R*-tree based clustering methods for images, which has been a tricky issue: although objects stored in the same R* tree leaf node enjoys spatial proximity, it is well-known that R* trees cannot be used directly for cluster analysis. Nevertheless, R* tree’s indexing feature can be used to assist existing cluster analysis methods, thus enhancing their performance of cluster quality. In this chapter, the authors report their progress of using R* trees to improve well-known K-means and hierarchical clustering methods. Based on R*-Tree’s feature of indexing Minimum Bounding Box (MBB) according to spatial proximity, the authors extend R*-Tree’s application to cluster analysis containing image data. Two improved algorithms, KMeans-R and Hierarchy-R, are proposed. Experiments have shown that KMeans-R and Hierarchy-R have achieved better clustering quality.

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