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International Joint Conference on Neural Networks (IJCNN)

作者： Ming Dong Jing Wu Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

Support vector machines (SVMs) have been promising methods in pattern recognition because of their solid mathematical foundation. In this paper, we propose a localized SVM classification scheme (LSVM). In which we first cluster the training data in each category, and then train a set of SVMs based on these dusters. The SVMs trained from the clusters in each category that are nearest to the given input pattern are then selected for the final classification. Our experiments on six UCI datasets show that LSVM outperforms the traditional SVM.

关键词： Support vector machines Support vector machine classification Kernel USA Councils Solids machine learning Training data Nails Neural networks Clustering algorithms

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Region-based Image Annotation using Asymmetrical Support Vector machine-based Multiple-Instance Learning

Region-based Image Annotation using Asymmetrical Support Vec...

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Conference on Computer vision and pattern recognition (CVPR)

作者： Changbo Yang Ming Dong Jing Hua Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.

关键词： machine learning Support vector machines Image retrieval pattern recognition Computer science Training data Supervised learning Computer vision Feature extraction Image segmentation

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Co-Clustering Image Features and Semantic Concepts

Co-Clustering Image Features and Semantic Concepts

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IEEE International Conference on Image Processing

作者： Manjeet Rege Ming Dong Farshad Fotouhi Department of Computer Science Machine Vision & Pattern Recognition Laboratory Wayne State University Detroit MI USA Database & Multimedia Systems Group Wayne State University Detroit MI USA

In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.

关键词： Feedback Image retrieval Bipartite graph Image databases Clustering algorithms machine vision pattern recognition Multimedia databases Spatial databases Multimedia systems

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Finding a Semantic Structure Interactively in Image Databases

Finding a Semantic Structure Interactively in Image Database...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision & Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Database & Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI USA

We present a new approach to organize an image database by finding a semantic structure interactively based on multi-user relevance feedback. By treating user relevance feedbacks as weak classifiers and combining them together, we are able to capture the categories in the users' mind and build a semantic structure in the image database. Experiments performed on an image database consisting of general purpose images demonstrate that our system outperforms some of the other conventional methods

关键词： Image databases Feedback Image retrieval machine learning algorithms Support vector machines Support vector machine classification Information retrieval machine vision pattern recognition Multimedia databases

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Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

Co-clustering Documents and Words Using Bipartite Isoperimet...

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IEEE International Conference on Data Mining (ICDM)

作者： Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision and Pattern Recognition Laboratory Database and Multimedia Systems GroupDepartment of Computer Science Wayne State University Detroit MI USA Database and Multimedia Systems Group Database and Multimedia Systems GroupDepartment of Computer Science Wayne State University Detroit MI USA

In this paper, we present a novel graph theoretic approach to the problem of document-word co-clustering. In our approach, documents and words are modeled as the two vertices of a bipartite graph. We then propose isoperimetric co-clustering algorithm (ICA) - a new method for partitioning the document-word bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over spectral approach in terms of the quality, efficiency and stability in partitioning the document-word bipartite graph.

关键词： Bipartite graph Partitioning algorithms Independent component analysis Equations Eigenvalues and eigenfunctions Stability Data mining Random variables Mutual information machine vision

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A threshlod selection method based on multiscale and graylevel co-occurrence matrix analysis

A threshlod selection method based on multiscale and graylev...

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International Conference on Document Analysis and recognition

作者： Yun Li Mohamed Cheriet C.Y. Suen GM606 Center for Pattern Recognition and Machine Intelligence Concordia University Montreal QUE Canada Laboratory for Imagery Vision and Artificial Intelligence Ecole de Technologie Supérieure University of Quebec Montreal QUE Canada

Noise and complex backgrounds often make the thresholding of degraded document images difficult. In this paper, we propose a new threshold selection method to handle severely degraded document images. First, multiscale image description is adopted to analyze the image edge. From this, edge pixel pair information is derived and recorded by a graylevel co-occurrence matrix. An appropriate threshold value is chosen by measuring the edge pixel pair co-occurrence matrix. The new method is tested with degraded document images. The experimental results show it is resistant to noise and complex backgrounds.

关键词： Laplace equations Degradation Image analysis Image storage Text analysis Image edge detection pattern recognition machine intelligence Laboratories Artificial intelligence

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A threshlod selection method based on multiscale and graylevel co-occurrence matrix analysis

A threshlod selection method based on multiscale and graylev...

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8th International Conference on Document Analysis and recognition

作者： Li, Yun Cheriet, Mohamed Suen, Ching Y. Center for Pattern Recognition and Machine Intelligence Concordia University GM606 1455 de Maisonneuve Blvd. West Montreal Que. H3G 1M8 Canada Laboratory for Imagery Vision and Artificial Intelligence Ecole de Technologie Supérieure University of Quebec 1100 Notre-Dame West Montreal Que. H3C 1K3 Canada

ISBN: (纸本)0769524206

Noises and complex backgrounds often make the thresholding of degraded document images a difficult job. In this paper, we propose a new threshold selection method to handle severely degraded document images. First, multiscale image description is adopted to analyze the image edge. From it, edge pixel pair information is derived and recorded by a graylevel co-occurrence matrix. An appropriate threshold value is chosen by measuring the edge pixel pair co-occurrence matrix. The new method is tested with degraded document images. The experimental results show it is resistant to noises and complex backgrounds. © 2005 IEEE.

关键词： Threshold elements

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A segmentation method for touching italic characters

A segmentation method for touching italic characters

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International Conference on pattern recognition

作者： Yun Li S. Naoi M. Cheriet C.Y. Suen Center of Pattern Recognition and Machine Intelligence Concordia University Montreal QUE Canada Document Processing Laboratory Fujitsu Laboratories Limited Kawasaki Japan Laboratory for Imagery Vision and Artificial Intelligence Ecole de Technologie Supérieure University of Quebec Montreal QUE Canada

ISBN: (纸本)0769521282

Segmentation is an essential part of a recognition system. It is difficult to handle touching characters, especially for italic fonts. We present a method to achieve the accurate segmentation of touching italic characters. It is free of slant correction, so extra noises will not be introduced. We use slant projection and contour analysis to find the segmentation points. Then the shortest path approach is adopted for accurately locating the cut path of each candidate segmentation point. Based on dynamic programming, we can find the best segmentation result from those cut paths. By this method, we can improve the recognition rate on italic fonts.

关键词： Image segmentation Histograms pattern recognition Image recognition Dynamic programming Character recognition machine intelligence Laboratories Artificial intelligence Optical character recognition software

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Classifiability based omnivariate decision trees

Classifiability based omnivariate decision trees

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International Joint Conference on Neural Networks (IJCNN)

作者： Y. Li M. Dong Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

Decision trees represent a simple and powerful method of induction from labeled examples. Univariate decision trees consider the value of a single attribute at each node, leading to the splits that are parallel to the axes. In linear multivariate decision trees, all the attributes are used and the partition at each node is based on a linear discriminate (a hyperplane). Nonlinear multivariate decision trees are able to divide the input space arbitrarily based on higher order parameterizations of the discriminate, though one should be aware of the increase of the complexity and the decrease in the number of examples available as moves further down the tree. In omnivariate decision trees, the decision node may be univariate, linear, or nonlinear. Such architecture frees the designer from choosing the appropriate tree type for a given problem. In this paper, we propose to do the model selection at each decision node based on a novel classifiability measure when building omnivariate decision trees. The classifiability measure captures the possible sources of misclassification with relative ease and is able to accurately reflect the complexity of subproblems at each node. The proposed approach does not require the time consuming statistic tests at each node and therefore does not suffer from as high computational burden as typical model selection algorithm. Our simulation results over several data sets indicate that our approach can achieve at least as good classification accuracy as statistical tests based model select algorithms, but in much faster speed.

关键词： Classification tree analysis Decision trees Testing Shape machine vision pattern recognition Laboratories Computer science Buildings Statistical analysis

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Communication via eye blinks and eyebrow raises: Video-based human-computer interfaces

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Universal Access in the Information Society 2003年第4期2卷 359-373页

作者： Grauman, K. Betke, M. Lombardi, J. Gips, J. Bradski, G.R. Vision Interface Group AI Laboratory Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Computer Science Department Boston University 111 Cummington St BostonMA02215 United States EagleEyes Computer Science Department Boston College Fulton Hall Chestnut HillMA02467 United States Vision Graphics and Pattern Recognition Microcomputer Research Laboratory Intel Corporation SC12-303 2200 Mission College Blvd Santa ClaraCA95054-1537 United States

Two video-based human-computer interaction tools are introduced that can activate a binary switch and issue a selection command. "BlinkLink," as the first tool is called, automatically detects a user's eye blinks and accurately measures their durations. The system is intended to provide an alternate input modality to allow people with severe disabilities to access a computer. Voluntary long blinks trigger mouse clicks, while involuntary short blinks are ignored. The system enables communication using "blink patterns:" sequences of long and short blinks which are interpreted as semiotic messages. The second tool, "EyebrowClicker," automatically detects when a user raises his or her eyebrows and then triggers a mouse click. Both systems can initialize themselves, track the eyes at frame rate, and recover in the event of errors. No special lighting is required. The systems have been tested with interactive games and a spelling program. Results demonstrate overall detection accuracy of 95.6% for BlinkLink and 89.0% for EyebrowClicker. © Springer-Verlag 2003.

关键词： Computer vision

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