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A Robust Descriptor based on Weber's Law

A Robust Descriptor based on Weber's Law

引用

26th IEEE Conference on computer vision and Pattern Recognition (CVPR 2008), vol.8

作者： Jie Chen Shiguang Shan Guoying Zhao Xilin Chen Wen Gao Matti Pietikainen School of Computer Science and Technology Harbin Institute of Technology Harbin China Machine Vision Group Department of Electrical and Information Engineering University of Oulu Finland Languages Informatics Systems and Software Engineering Department Faculty of Computer Science Chinese Academy and Sciences Beijing China

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends on not only the change of a stimulus (such as sound, lighting, et al.) but also the original intensity of the stimulus. Specifically, WLD consists of two components: its differential excitation and orientation. A differential excitation is a function of the ratio between two terms: One is the relative intensity differences of its neighbors against a current pixel;the other is the intensity of the current pixel. An orientation is the gradient orientation of the current pixel. For a given image, we use the differential excitation and the orientation components to construct a concatenated WLD histogram feature. Experimental results on Brodatz textures show that WLD impressively outperforms the other classical descriptors (e.g., Gabor). Especially, experimental results on face detection show a promising performance. Although we train only one classifier based on WLD features, the classifier obtains a comparable performance to state-of-the-art methods on MIT+CMU frontal face test set, AR face dataset and CMU profile test set.

关键词： Robustness Face detection Humans Content addressable storage Testing Psychology Laboratories machine intelligence Information processing computers

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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

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

引用

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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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 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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A context-sensitive and user-centric approach to developing personal assistants

A context-sensitive and user-centric approach to developing ...

引用

2005 AAAI Spring Symposium

作者： Des Jardins, Marie Eaton, Eric Wagstaff, Kiri University of Maryland Baltimore County Computer Science and Electrical Engineering Department 1000 Hilltop Circle Baltimore MD 21250 Jet Propulsion Laboratory Machine Learning Systems Group 4800 Oak Grove Drive Pasadena CA 91109

Several ongoing projects in the MAPLE (Multi-Agent Planning and learning) lab at UMBC and the machine learning systems group at JPL focus on problems that we view as central to the development of persistent agents. This position paper describes our current research in this area, focusing on four topics in particular: effective use of observational and active learning, utilizing repeated behavioral contexts, clustering with annotated constraints, and learning user preferences. Copyright © 2005, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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Sparse greedy minimax probability machine classification

Sparse greedy minimax probability machine classification

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17th Annual Conference on Neural Information Processing systems, NIPS 2003

作者： Strohmann, Thomas R. Belitski, Andrei Grudic, Gregory Z. DeCoste, Dennis Department of Computer Science University of Colorado Boulder United States NASA Jet Propulsion Laboratory Machine Learning Systems Group United States

ISBN: (纸本)0262201526

The Minimax Probability machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrices of the classes exist. However, as with Support Vector machines, MPMC is computationally expensive and requires extensive cross validation experiments to choose kernels and kernel parameters that give good performance. In this paper we address the computational cost of MPMC by proposing an algorithm that constructs nonlinear sparse MPMC (SMPMC) models by incrementally adding basis functions (i.e. kernels) one at a time - greedily selecting the next one that maximizes the accuracy bound Ω. SMPMC automatically chooses both kernel parameters and feature weights without using computationally expensive cross validation. Therefore the SMPMC algorithm simultaneously addresses the problem of kernel selection and feature selection (i.e. feature weighting), based solely on maximizing the accuracy bound Ω. Experimental results indicate that we can obtain reliable bounds Ω, as well as test set accuracies that are comparable to state of the art classification algorithms.

关键词： Covariance matrix

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Sparse greedy minimax probability machine classification 03

Sparse greedy minimax probability machine classification

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Proceedings of the 16th International Conference on Neural Information Processing systems

作者： Thomas R. Strohmann Andrei Belitski Gregory Z. Grudic Dennis DeCoste Department of Computer Science University of Colorado Boulder Machine Learning Systems Group NASA Jet Propulsion Laboratory

The Minimax Probability machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrices of the classes exist. However, as with Support Vector machines, MPMC is computationally expensive and requires extensive cross validation experiments to choose kernels and kernel parameters that give good performance. In this paper we address the computational cost of MPMC by proposing an algorithm that constructs nonlinear sparse MPMC (SMPMC) models by incrementally adding basis functions (i.e. kernels) one at a time – greedily selecting the next one that maximizes the accuracy bound Ω. SMPMC automatically chooses both kernel parameters and feature weights without using computationally expensive cross validation. Therefore the SMPMC algorithm simultaneously addresses the problem of kernel selection and feature selection (i.e. feature weighting), based solely on maximizing the accuracy bound Ω. Experimental results indicate that we can obtain reliable bounds Ω, as well as test set accuracies that are comparable to state of the art classification algorithms.

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Classifying uncovered examples by rule stretching 1

引用

11th International Conference on Inductive Logic Programming, ILP 2001

作者： Eineborg, Martin Boström, Henrik Machine Learning Group Department of Computer and Systems Sciences Stockholm University/Royal Institute of Technology Electrum 230 Stockholm Sweden Virtual Genetics Laboratory Stockholm171 77 Sweden

ISBN: (数字)9783540447979

ISBN: (纸本)9783540425380

This paper is concerned with how to classify examples that are not covered by any rule in an unordered hypothesis. Instead of assigning the majority class to the uncovered examples, which is the standard method, a novel method is presented that minimally generalises the rules to include the uncovered examples. The new method, called Rule Stretching, has been evaluated on several domains (using the inductive logic programming system Virtual Predict for induction of the base hypothesis). The results show a significant improvement over the standard method. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Inductive logic programming (ILP)

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Editorial

引用

Pattern Recognition 2001年第8期34卷 1513-1513页

作者： Vito Roberto Emanuele Trucco Machine Vision Laboratory Department of Informatics University od Udine Via delle Scienze 206 I 33100 Udine Italy Computer Vision Group and Ocean Systems Laboratory Heriot-Watt University Riccarton Edinburgh EH14 4AS UK

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