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共 276 条记录，以下是101-110 订阅

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A Comparison of Genetic Programming Feature Extraction Languages for image Classification

A Comparison of Genetic Programming Feature Extraction Langu...

引用

ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Maghoumi, Mehran Ross, Brian J. Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada

ISBN: (纸本)9781479945030

Visual pattern recognition and classification is a challenging computer vision problem. Genetic programming has been applied towards automatic visual pattern recognition. One of the main factors in evolving effective classifiers is the suitability of the GP language for defining expressions for feature extraction and classification. This research presents a comparative study of a variety of GP languages suitable for classification. Four different languages are examined, which use different selections of image processing operators. One of the languages does block classification, which means that an image is classified as a whole by examining many blocks of pixels within it. The other languages are pixel classifiers, which determine classification for a single pixel. Pixel classifiers are more common in the GP-vision literature. We tested the languages on different instances of Brodatz textures, as well as aerial and camera images. Our results show that the most effective languages are pixel-based ones with spatial operators. However, as is to be expected, the nature of the image will determine the effectiveness of the language used.

关键词： computer vision feature extraction genetic algorithms image classification image texture Brodatz textures GP language GP-vision literature aerial images camera images classifiers computer vision problem genetic programming feature extraction languages image processing operators pixel classifiers spatial operators visual pattern recognition Boats Feature extraction Genetic programming Pattern recognition Standards Testing Training image classification Feature extraction Genetic Algorithm (GA) images Visual pattern recognition computer vision space operation Language Classifiers aerial images image texture Boats Pattern recognition Functional training

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Music Classification using Extreme Learning Machines

Music Classification using Extreme Learning Machines

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8th International symposium on image and signal processing and Analysis (ISPA)

作者： Scardapane, Simone Comminiello, Danilo Scarpiniti, Michele Uncini, Aurelio Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun DIET I-00184 Rome Italy

ISBN: (纸本)9789531841948;9789531841870

Over the last years, automatic music classification has become a standard benchmark problem in the machine learning community. This is partly due to its inherent difficulty, and also to the impact that a fully automated classification system can have in a commercial application. In this paper we test the efficiency of a relatively new learning tool, Extreme Learning Machines (ELM), for several classification tasks on publicly available song datasets. ELM is gaining increasing attention, due to its versatility and speed in adapting its internal parameters. Since both of these attributes are fundamental in music classification, ELM provides a good alternative to standard learning models. Our results support this claim, showing a sustained gain of ELM over a feedforward neural network architecture. In particular, ELM provides a great decrease in computational training time, and has always higher or comparable results in terms of efficiency.

关键词： audio signal processing feedforward neural nets information retrieval learning (artificial intelligence) music signal classification

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Life-long Semi-supervised Learning: Continuation of Both Learning and Recognition

Life-long Semi-supervised Learning: Continuation of Both Lea...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Youki Kamiya Toshiaki Ishii Osamu Hasegawa Department of Computational Intelligence and Systems Science Interdisciplinary Graduate Tokyo Institute of Technology Kanagawa Japan Imaging Science and Engineering Laboratory Tokyo Institute of Technology Japan

This paper presents a new method for continuous and incremental learning and recognition based on self-organized incremental neural networks. It is available in the fluctuating environment where the number of recognition classes cannot be defined. In this method, the learning process and recognition process are not separated. This method can acquire concept when multiple feature vectors of new input object come, and then can recognize it using previously acquired concept. We experiment an examples of life-long semi-supervised learning tasks in real world. In the result, the proposed method was able to learn and recognize 104 objects incrementally, non-stop, and in real time

关键词： Semisupervised learning Neural networks computational intelligence image recognition Humans Pattern recognition Data analysis signal processing Laboratories Training data

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A Modified Fuzzy C-Means Algorithm with Adaptive Spatial Information for Color image Segmentation

A Modified Fuzzy C-Means Algorithm with Adaptive Spatial Inf...

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ieee symposium on computational intelligence in image and signal processing

作者： Yu, Zhiding Zou, Ruobing Yu, Simin Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781424427604

Though FCM has long been widely used in image segmentation, it yet faces several challenges. Traditional FCM needs a laborious process to decide cluster center number by repetitive tests. Moreover, random initialization of cluster centers can let the algorithm easily fall onto local minimum, causing the segmentation results to be suboptimal. Traditional FCM is also sensitive to noise due to the reason that the pixel partitioning process goes completely in the feature space, ignoring some necessary spatial information. In this paper we introduce a modified FCM algorithm for color image segmentation. The proposed algorithm adopts an adaptive and robust initialization method which automatically decides initial cluster center values and center number according to the input image. In addition, by deciding the window size of pixel neighbor and the weights of neighbor memberships according to local color variance, the proposed approach adaptively incorporates spatial information to the clustering process and increases the algorithm robustness to noise pixels and drastic color variance. Experimental results have shown the superiority of modified FCM over traditional FCM algorithm.

关键词： Pixels

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Hierarchical Face Clustering using SIFT image Features

Hierarchical Face Clustering using SIFT Image Features

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Panagiotis Antonopoulos Nikos Nikolaidis Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

In this paper an algorithm to cluster face images found in video sequences is proposed. A novel method for creating a dissimilarity matrix using SIFT image features is introduced. This dissimilarity matrix is used as an input in a hierarchical average linkage clustering algorithm, which yields the clustering result. Three well known clustering validity measures are provided to asses the quality of the resulting clustering, namely the F measure, the overall entropy (OE) and the Gamma statistic. The final result is found to be quite robust to significant scale, pose and illumination variations, encountered in facial images

关键词： Face detection Clustering algorithms Humans Hidden Markov models signal processing algorithms Video sequences Face recognition Couplings Clustering methods Testing

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Spatial and frequency domain comparison of interpolation techniques in digital image processing

Spatial and frequency domain comparison of interpolation tec...

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10th International symposium of Hungarian Researchers on computational intelligence and Informatics, CINTI 2009

作者： Póth, Miklós Szakáll, Tibor Department of Informatics Subotica Tech Serbia

This article presents a comparison of different interpolation techniques both in the spatial and frequency domains. Various methods are presented, from a very simple linear interpolation to more complex cubic convolution. The Fourier analysis is used to compare the methods in the frequency domain. Methods are compared by doubling the images in both spatial directions and calculating the signal-to-noise ratio between the original and interpolated images. The paper will also include some notes on subjective image quality.

关键词： Interpolation

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A Multi-Window Stereo Vision Algorithm With Improved Performance at Object Borders

A Multi-Window Stereo Vision Algorithm With Improved Perform...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Jun Zhao Jayantha Katupitiya ARC Centre of Excellence for Autonomous Systems University of New South Wales Sydney NSW Australia

Conventional correlation based stereo vision algorithms have poor performance at object borders due to occlusion. In this paper, a new matching sequence has been introduced. To detect left object border, a left shifted window is used with right image as the reference image and process from left to right. This method gives excellent left object border detection while detection of right object border is poor. The right object borders can be detected with equal success using a right shifted window with left image as reference and process from right to left. The combination result is good detection at both object borders

关键词： Stereo vision Object detection signal processing algorithms Pixel computational intelligence Manufacturing processes Australia Performance evaluation Testing Shape

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A Novel Approach to Collect Training images from WWW for image Thesaurus Building

A Novel Approach to Collect Training Images from WWW for Ima...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Joohyoun Park Jongho Nang Department of Computer Science and Engineering Sogang University Seoul South Korea

This paper introduces a novel approach to change gathered images from WWW into training images to build an image thesaurus. The requirements for being training images are a large number of images and with highly relevant to a given concept. To fulfil these requirements, a system should be able to collect a large number of relevant images to a given concept from WWW by the proposed criterion of relevance to the concept for each image. Then, the irrelevant images would be filtered out by the modified hierarchical clustering method based on the weighted combination of 5 MPEG-7 visual descriptors and the proposed criterion of relevance to the concept for each cluster. Upon experimental results, the precision of the set of images generated by the proposed method is about 18% higher than that of the set of images generated by other methods

关键词： World Wide Web Thesauri image generation HTML image analysis MPEG 7 Standard image retrieval Content based retrieval computational intelligence signal processing

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Developing Metrics for Evolving a Musically Satisfying Result from a Population of Computer Generated Music Compositions

Developing Metrics for Evolving a Musically Satisfying Resul...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Kris Bryden Department of Music Iowa State University Ames IA USA

Creating a computational metric of the quality and musicality of a computer generated music composition is a challenging problem. This paper describes a possible way to develop numerically based metrics for standardizing the fitness evaluation of a population of computer generated music compositions. These metrics are based on assertions by music theorists about the dynamic forces within music that provides the music with a sense of motion and phrasing. The actions of several musical parameters are ranked according to the sense of intensity they produce. This process is applied to an acoustic music composition, and the values are then plotted on a graph. The composite curve from these values give the evaluator a sense of the patterns of lower and higher degrees of intensity, thus providing a sense of how well the music provides a sense of motion and phrasing

关键词： Neural networks Multiple signal classification signal generators Music Humans computational intelligence signal processing Application software Acoustic applications Calculus

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A Fast Algorithm for HEVC Screen Content Coding

A Fast Algorithm for HEVC Screen Content Coding

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2019 ieee symposium Series on computational intelligence, SSCI 2019

作者： Tang, Hao Yang Wei Duan, Yi Sun, Lin Li Ran Li, Yi Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781728124858

As a high efficiency video coding (HEVC) extension, screen content coding (SCC) has a significant effect in the compressed screen content, but causes a problem encoder calculates a higher complexity. This paper proposes a fast decision method based on HEVC screen content coding. The image content is divided into a natural coding unit (NCU) and a screen coding unit (SCU). The image gradients of different properties are different, and the gini impurity classification gradient value is used to speed up the encoding speed. The experimental results show that under the frame configuration of HM-15.0 SCM-2.0, the method can save about 41.4% of the encoder time. © 2019 ieee.

关键词： Video signal processing

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