A Novel Fast Fractal Image Compression Method Based on Distance Clustering in High Dimensional Sphere Surface

Liu, Shuai; Zheng, Pan; Cheng, Xiaochun

doi:10.1142/s0218348x17400047

Cited by 117 publications

(45 citation statements)

References 33 publications

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“…Suppose Θ represents a recognition framework, the basic probability assignment (BPA) on Θ is defined as m ′ : 2 Θ cc in Equation 16. Its power set 2 Θ represents all possible sets of propositions, which is a set composed of all subsets of Θ.…”

Section: Multi-feature Ct Image Recognition Methods Based On Deep Learmentioning

confidence: 99%

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Multi‐feature data mining for CT image recognition

Yong-lian

2018

Concurrency and Computation

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Recognition method of CT images based on color, morphology, and texture is inaccurate and unreliable recently. Therefore, a medical image recognition method based on data mining is proposed with a multi-feature fusion in this paper. First, image acquisition method is determined by analyzing the environment of CT image recognition. Then, acquired CT image is standardized and whitened to reduce redundant information. Moreover, based on feature of the color, texture, and height in preprocessed CT images, a deep neural network is trained by using a large amount of image data in normal scene. The deep learning classifier is fine-tuned by using the marked multi-feature CT image data. Finally, output recognition result is obtained according to the classification decision threshold. Experimental results show that the correct recognition rate of the proposed method can reach more than 98%. The accuracy rate is higher and the stability of proposed method is better by comparing with traditional CT image recognition methods. KEYWORDSCT images, data mining, deep learning, image recognition, multiple feature INTRODUCTIONIn recent years, with the rapid development of medical imaging technology, it improved to big medical image analysis. Today, many big database are constructed. How we can extract useful information from massive medical image data in the database has brought great challenges to medical image recognition since traditional database technology cannot provide enough service for the large database. Data mining is used to obtain effective information in medical image database. CT image recognition is a multidisciplinary cross-domain of comprehensive medical imaging, mathematical modeling, and computer technology. 1,2 Today, there are two new issues that are brought because of large scale of multi-feature CT image. One issue is CT image data that has a higher dimension and requires a model with stronger learning adaptability. The other is big CT image data that is more fragmented with more complex data structure, which requires integration of huge different information. 3 Such requirements cannot be solved by traditional data analysis methods. Therefore, how we extract useful information from massive CT image data has become a research hotspot. With the rapid development and popularization of CT imaging equipment, CT image data have increased explosively. How we can efficiently and accurately analyze CT image has become a major challenge. 4 Furthermore, computer-assisted CT medical image analysis improves the efficiency and accuracy of the analysis and diagnosis of CT image. Therefore, computer-aided detection and diagnosis are becoming a cross-sectional research area that receives more and more attention.A multi-feature CT medical image recognition method was proposed based on polarization spectrum. 5 The method used indoor imaging spectrometer FISS-P equipped with polarizers to collect the polarization spectrum images, including general images and five kinds of CT medical images indoors. They recognized CT images by ...

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Section: Multi-feature Ct Image Recognition Methods Based On Deep Learmentioning

confidence: 99%

“…16 Because CT medical image recognition is greatly affected by the environment, especially, the light intensity heavily affects the color and shade of the image. It is necessary to reduce the dimension of image features and extract the main features of the image in pattern recognition.…”

Section: Feature Extraction Of Multi-feature Ct Medical Imagesmentioning

confidence: 99%

Multi‐feature data mining for CT image recognition

Yong-lian

2018

Concurrency and Computation

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“…Therefore, some novel techniques on fractal image compression and fractal encoding are proposed in [26,27]. In addition, a generalization of the Hurst estimation approach with q-th order moments of the distribution of the increments are used to characterize the statistical evolution of the series in [13].…”

Section: Fbmmentioning

confidence: 99%

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

2018

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Abstract:The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA) method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths ∆h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

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“…To address this issue, Liu et al distance clustering in high dimensional sphere surface is applied to speedup fractal compression method. 16 Correctness and effectiveness properties of the mentioned method are analyzed. Also, positive performance is shown by experimental results.…”

Section: Overview Of Work In This Thematic Issuementioning

confidence: 99%

Editorial

Liu

2017

Fractals

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Fractal represents a special feature of nature and functional objects. However, fractal based computing can be applied to many research domains because of its fixed property resisted deformation, variable parameters and many unpredictable changes. Theoretical research and practical application of fractal based computing have been hotspots for 30 years and will be continued. There are many pending issues awaiting solutions in this domain, thus this thematic issue containing 14 papers publishes the state-of-the-art developments in theorem and application of fractal based computing, including mathematical analysis and novel engineering applications. The topics contain fractal and multifractal features in application and solution of nonlinear odes and equation.

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A Novel Fast Fractal Image Compression Method Based on Distance Clustering in High Dimensional Sphere Surface

Cited by 117 publications

References 33 publications

Multi‐feature data mining for CT image recognition

Multi‐feature data mining for CT image recognition

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

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