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Data mining techniques are used for mining useful trends or patterns from textual and image data sets. Medical data mining is very important field as it has significant utility in healthcare domain in the real world. The mining techniques can help the healthcare industry to improve quality of services and grow faster with state-of-the-art technologies. Technology usage is not limited to decision making...
The main information of image focus in the target area, and the rest part contains a large amount of redundancy. The image segmentation is an important technology in image processing. This paper presents an improved rough set image segmentation algorithm, which is based on the theory of fuzzy C-means clustering, the human visual attention model and relative position. Combination of fuzzy clustering...
Text line extraction is an important part of document image analysis. It provides significant information for follow-up character recognition and text-based retrieval. By analyzing the layout style and writing features of a document image with radicalized Bagua layout in Jiugong in Shui script, we propose a multi-directional text partition method for Shui Script based on Delaunay triangular mesh and...
Nowadays everywhere remote sensing images are used for wide variety of applications, creation of mapping products for military and civil applications, evaluation of environmental damage, monitoring of land use, radiation monitoring, urban planning, growth regulation, soil assessment, and crop yield appraisal. A few number of image classification algorithms have proved good precision in classifying...
Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. In the supervised FS methods...
Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. In the supervised FS methods...
Interpretation of seismic data is a time-consuming and arduous task. Clustering analysis as an intelligent analysis method can be applied to the petroleum industry. While most clustering algorithms have good performance on transactional data, they are not suitable for seismic data. Unlike traditional data, seismic data have some characteristics of its own: spatially position, fuzzy nature and arbitrary...
A novel method for texture segmentation based on fuzzy C-means(FCM) algorithm combined with gray level co-occurrence matrices(GLCM) and space information is proposed. In the proposed method, four statistics extracted from GLCM of the image are used as texture features of the image. And these texture features combining with two space information features extracted from the image are used as features...
Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. In this work, Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in...
Clustering or data grouping is a key initial procedure in image processing. In present scenario the size of database of companies has increased dramatically, these databases contain large amount of text, image. They need to mine these huge databases and make accurate decisions in short durations in order to gain marketing advantage. As image is a collection of number of pixels. It is difficult to...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Chongming Dongtan national nature reserve is an internationally important wetland. Accurate survey of invasive alien vegetation and indigenous vegetation is the foundation of the study on the ecological impact of invasive vegetation, wetland protection and management. The high spatial resolution images have more spatial details and texture characteristics, and offer the possibility to extract information...
Juniperus excelsa subsp. Polycarpos, which Iranians know as the Persian juniper, located in the northeast of Iran. The survey of forest resources is an important task for the management and protection of the forest. Traditionally, such an essential task is heavily dependent on the labor-intensive ground survey. In this paper, we must be able to extract tree density by two methods and compare with...
As a main factor of evaluating city development speed, road is one of the fast information updating element during city development. Road information extraction based on high-resolution remotely sensed images has very important significance because road affects city land use-cover change. Based on the synthetically analyzing all kinds of road extraction method from high-resolution remotely sensed...
Remote sensing information extraction is the key step of remote sensing application, and the automatic and high-precise extraction of water information from remotely sensed images is of great significance and urgently required in many research fields. This paper presents a step-by-step iterative transformation mechanism to extract water information, which uses spatial scale transformation mechanism...
Clustering is a useful approach in data mining, image segmentation, and other problems of pattern recognition. Fuzzy clustering process can be quite slow when there are many objects or pattern to be clustered. This article discusses about an algorithm, ckMeans, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting partition quality. The reduction...
An effective approach to extracting geometric information of scenes from a single uncalibrated image is presented in this paper. Without any prior knowledge of the camera, we describe how to employ cross ratio to compute the length of a line segment on plane surface, while only the length(s) of the certain segment(s) is/are known. Real images of indoor and outdoor scene tests validate our approach.
The paper realizes water body extraction well using object-oriented classification method based on SPOT5 image. Firstly, segment image according to multi-scale image segmentation method based on edge-detection algorithm. Secondly, determine various characteristic parameters in land surface features according to object characteristics such as spectrum, shape and texture. And thirdly, use SVM (Support...
Land use/land cover classification in the south of the Gaoligong Mountain based on Landsat TM by use of object-oriented classification technique are presented. Through several key steps, such as image pre-processing, feature extract, multi-segmentation, and image classification, Land use/Land cover in the mountainous area is successfully mapped. The accuracy assessment indicates that the object-oriented...
This paper focuses on the issue of improving the quality of low level 2D feature extraction for human action recognition. For instance, existing algorithms such as the Optical Flow algorithm detects noisy and irrelevant features because of its lack of ground truth data sets for complex scenes. For these features, it is difficult to extract data such as coordinate positions of the features, velocity...
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