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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Wang, Hongxia Zeng, Long Peng, Dewei Geng, Feng Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781467365932

Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding, this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in Locality-constrained Linear Coding (LLC) scheme. Locality-constrained principal component linear coding can solve the multicollinearity and improves the classification accuracy, but it increases the time overhead of the coding. This paper presents an improved scheme called Locality-constrained linear coding based on the principal components of visual vocabulary. To determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual vocabulary. Experiments have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4 dataset. Experimental results show that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of Locality-constrained principal component linear coding.

关键词： visual object categorization local feature linear coding principal components

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Locality-constrained Linear Coding Based on Principal Components of visual Vocabulary for visual object categorization

Locality-constrained Linear Coding Based on Principal Compon...

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The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

作者： Hongxia Wang Long Zeng Dewei Peng Feng Geng School of Computer Science & Technology Wuhan University of Technology Wuhan HubeiChina

Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding,this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in Locality-constrained Linear Coding(LLC)***-constrained principal component linear coding can solve the multicollinearity and improves the classification accuracy,but it increases the time overhead of the *** paper presents an improved scheme called Locality-constrained linear coding based on the principal components of visual *** determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual *** have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4 *** results show that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of Localityconstrained principal component linear coding.

关键词： visual object categorization local feature linear coding principal components

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Locality-constrained Linear Coding Based on Principal Components of visual Vocabulary for visual object categorization

Locality-constrained Linear Coding Based on Principal Compon...

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第十四届分布式计算及其应用国际学术研讨会

作者： Hongxia Wang Long Zeng Dewei Peng Feng Geng School of Computer Science & Technology Wuhan University of Technology

Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding, this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in Locality-constrained Linear Coding(LLC) scheme. Locality-constrained principal component linear coding can solve the multicollinearity and improves the classification accuracy, but it increases the time overhead of the coding. This paper presents an improved scheme called Locality-constrained linear coding based on the principal components of visual vocabulary. To determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual vocabulary. Experiments have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4 dataset. Experimental results show that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of Localityconstrained principal component linear coding.

关键词： visual object categorization local feature linear coding principal components

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Fast image classification, by boosting fuzzy classifiers

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INFORMATION SCIENCES 2016年 327卷 175-182页

作者： Korytkowski, Marcin Rutkowski, Leszek Scherer, Rafal Czestochowa Tech Univ Inst Computat Intelligence PL-42200 Czestochowa Poland

This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes. Boosting meta-learning is used to find the most representative local features. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives better classification accuracy and the time of learning and testing process is more than 30% shorter. (C) 2015 Elsevier Inc. All rights reserved.

关键词： visual object categorization Fuzzy classification Boosting meta-learning

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visual object categorization with indefinite kernels in discriminant analysis framework

Visual object categorization with indefinite kernels in disc...

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Conference on Multimedia Content Analysis, Management and Retrieval 2006

作者： Kosinov, S Marchand-Maillet, S Univ Geneva CVML Gen Dufour 24 Geneva Switzerland

ISBN: (纸本)081946113X

The major focus of this work is on the application of indefinite kernels in multimedia processing applications illustrated on the problem of content-based digital image analysis and retrieval. The term "indefinite" here relates to kernel functions associated with non-metric distance measures that are known in many applications to better capture perceptual similarity defining relations among higher level semantic concepts. This paper describes a kernel extension of distance-based discriminant analysis method whose formulation remains convex irrespective of the definiteness property of the underlying kernel. The presented method deploys indefinite kernels rendered as unrestricted linear combinations of hyperkernels to approach the problem of visual object categorization. The benefits of the proposed technique are demonstrated empirically on a real-world image data set, showing an improvement in categorization accuracy.

关键词： indefinite kernels discriminant analysis visual object categorization

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Human visual object categorization can be described by models with low memory capacity

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VISION RESEARCH 2003年第21期43卷 2265-2280页

作者： Peters, RJ Gabbiani, F Koch, C CALTECH Div Biol Pasadena CA 91125 USA Baylor Coll Med Div Neurosci Houston TX 77030 USA

Studies of high-level models of visual object categorization have left unresolved issues of neurobiological relevance, including how features are extracted from the image and the role played by memory capacity in categorization performance. We compared the ability of a comprehensive set of models to match the categorization performance of human observers while explicitly accounting for the models' numbers of free parameters. The most successful models did not require a large memory capacity, suggesting that a sparse, abstracted representation of category properties may underlie categorization performance. This type of representation-different from classical prototype abstraction-could also be extracted directly from two-dimensional images via a biologically plausible early-vision model, rather than relying on experimenter-imposed features. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： visual object categorization exemplar model multidimensional scaling similarity representation pairs triads

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