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Region Classification with Markov Field Aspect Models

Region Classification with Markov Field Aspect Models

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Jakob Verbeek Bill Triggs LJK INRIA Montbonnot France

Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-of-feature approaches label each pixel or patch independently. More advanced models attempt to improve the coherence of the labellings by introducing some form of inter-patch coupling: traditional spatial models such as MRF's provide crisper local labellings by exploiting neighbourhood-level couplings, while aspect models such as PLSA and LDA use global relevance estimates (global mixing proportions for the classes appearing in the image) to shape the local choices. We point out that the two approaches are complementary, combining them to produce aspect-based spatial field models that outperform both approaches. We study two spatial models: one based on averaging over forests of minimal spanning trees linking neighboring image regions, the other on an efficient chain-based Expectation Propagation method for regular 8-neighbor Markov random fields. The models can be trained using either patch-level labels or image-level keywords. As input features they use factored observation models combining texture, color and position cues. Experimental results on the MSR Cambridge data sets show that combining spatial and aspect models significantly improves the region-level classification accuracy. In fact our models trained with image-level labels outperform PLSA trained with pixel-level ones.

关键词： Labeling Image segmentation Pixel Linear discriminant analysis Animals Coherence Joining processes Markov random fields computer vision Layout

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Fast extraction of multi-resolution Gabor features

Fast extraction of multi-resolution Gabor features

引用

International Conference on Image Analysis and Processing

作者： Jarmo Ilonen Joni-Kristian Kamarainen Heikki Kalviainen Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology Finland Centre for Vision Speech and Signal Processing University of Surrey UK

Gabor filter responses are general purpose features for computer vision and image processing and have been very successful in many application areas, for example in bio- metric authentication (fingerprint matching, face detection, face recognition and iris recognition). In a typical feature construction, filters are utilised as a multi-resolution structure of several filters tuned to different frequencies and orientations. The multi-resolution structure is similar to wavelets, but the non-orthogonality of Gabor functions implies the main weakness: computational heaviness. The high computational complexity prevents their use in many real-time or near real-time tasks. In this study, an efficient sequential computation method for multi-resolution Gabor features is presented.

关键词： Gabor filters computer vision Image processing Application software Authentication Fingerprint recognition Face detection Face recognition Iris recognition Frequency

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Local and Weighted Maximum Margin Discriminant Analysis

Local and Weighted Maximum Margin Discriminant Analysis

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Haixian Wang Wenming Zheng Zilan Hu Sibao Chen Key Laboratory of Child Development and Learning Science of Ministry of Education South-East University Nanjing Jiangsu China School of Mathematics and Physics Anhui University of Science and Technology Maanshan Anhui China Department of Electronic Engineering and Information Science University of Science and Technology Hefei Anhui China

In this paper, we propose a new approach, called local and weighted maximum margin discriminant analysis (LWMMDA), to performing object discrimination. LWMMDA is a subspace learning method that identifies the underlying nonlinear manifold for discrimination. The goal of LWMMDA is to seek a transformation such that data points of different classes are projected as far as possible while points within a same class are as compact as possible. The projections are obtained by maximizing a new discriminant criterion, called local and weighted maximum margin criterion (LWMMC). Different from previous maximum margin criterion (MMC) which seeks only the globally Euclidean structure of data points, LWMMC takes the local property into account, which makes LWMMC more accurate in finding discriminant information. LWMMC has an additional weighted parameter β that further broadens the average margin between different classes. Computationally, LWMMDA completely avoids the singularity problem. Besides, LWMMDA couples the QR-decomposition into its framework, which makes LWMMDA very efficient and stable in implementation. Finally, LWMMDA framework is straightforwardly extended into the reproducing kernel Hilbert space induced by a nonlinear function ϕ. Experiments on digit visualization, face recognition, and facial expression recognition are presented to show the effectiveness of the proposed method.

关键词： Linear discriminant analysis Principal component analysis Face recognition pattern recognition Laplace equations Information analysis Performance analysis Educational technology Learning systems Kernel

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Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention

Beyond bottom-up: Incorporating task-dependent influences in...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Robert J. Peters Laurent Itti Departments of Computer Science University of Southern California Los Angeles CA USA Departments of Neuroscience and Psychology University of Southern California Los Angeles CA USA

A critical function in both machine vision and biological vision systems is attentional selection of scene regions worthy of further analysis by higher-level processes such as object recognition. Here we present the first model of spatial attention that (1) can be applied to arbitrary static and dynamic image sequences with interactive tasks and (2) combines a general computational implementation of both bottom-up (BU) saliency and dynamic top-down (TD) task relevance; the claimed novelty lies in the combination of these elements and in the fully computational nature of the model. The BU component computes a saliency map from 12 low-level multi-scale visual features. The TD component computes a low-level signature of the entire image, and learns to associate different classes of signatures with the different gaze patterns recorded from human subjects performing a task of interest. We measured the ability of this model to predict the eye movements of people playing contemporary video games. We found that the TD model alone predicts where humans look about twice as well as does the BU model alone; in addition, a combined BU*TD model performs significantly better than either individual component. Qualitatively, the combined model predicts some easy-to-describe but hard-to-compute aspects of attentional selection, such as shifting attention leftward when approaching a left turn along a racing track. Thus, our study demonstrates the advantages of integrating BU factors derived from a saliency map and TD factors learned from image and task contexts in predicting where humans look while performing complex visually-guided behavior.

关键词： Computational modeling Predictive models Humans Biology computing Biological system modeling Machine vision Layout Object recognition Image sequences Games

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Graph Commute Times for Image Representation

Graph Commute Times for Image Representation

引用

26th IEEE Conference on computer vision and pattern recognition (CVPR 2008), vol.12

作者： Regis Behmo Nikos Paragios Veronique Prinet Ecole Centrale Paris Malabry France NLPR/LIAMA Institute of Automation Chinese Academy and Sciences Beijing China

We introduce a new image representation that encompasses both the general layout of groups of quantized local invariant descriptors as well as their relative frequency. A graph of interest points clusters is constructed and we use the matrix of commute times between the different nodes of the graph to obtain a description of their relative arrangement that is robust to large intra class variation. The obtained high dimensional representation is then embedded in a space of lower dimension by exploiting the spectral properties of the graph made of the different images. Classification tasks can be performed in this embedding space. We expose classification and labelling results obtained on three different datasets, including the challenging PASCAL VOC2007 dataset. The performances of our approach compare favorably with the standard bag of features, which is a particular case of our representation.

关键词： Image representation Labeling Frequency Robustness computer vision Laplace equations Automation Shape Application software Graph theory

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Computational vision enhancement through genetic selection of biologically inspired receptive field geometry

Computational vision enhancement through genetic selection o...

引用

1999 International Conference on Information Intelligence and Systems, ICIIS 1999

作者： Perez, Claudio A. Salinas, Cristian Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago Chile

ISBN: (纸本)0769504469

The paper presents a genetic selection of biologically inspired receptive field geometry to improve pattern recognition in neural network classifiers. A genetic algorithm is employed to select the x, y dimensions and orientation of the receptive fields in a four hidden layer neural network with two planes per layer. Networks were ranked based on the fitness criterion: best generalization performance on a handwritten digit database. Results show strong correlation between the neural network performance and the receptive field x and y dimensions and orientation. The genetic algorithm improves classification performance by selecting appropriate receptive field size and orientation. The best receptive field configuration results outperformed those of perception based models. The proposed method allows a comparison among different architectures of receptive fields to find general patterns of improved performance. © 1999 IEEE.

关键词： Genetic algorithms

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Machine Dating of Handwritten Manuscripts

Machine Dating of Handwritten Manuscripts

引用

International Conference on Document Analysis and recognition

作者： U. Garain S. Parui T. Paquet L. Heutte Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire LITIS EA 4051 UFR des Sciences Université de Rouen Saint Etienne du Rouvray France

This paper presents a pioneering study on automatic dating of handwritten manuscripts. Analysis of handwriting style forms the core of the dating method. Initially, it is hypothesized that a manuscript can be dated, to a certain level of accuracy, by looking at the way it is written. The hypothesis is then verified with real samples of known dates. A general framework is proposed for machine dating of handwritten manuscripts. Experiments on a database containing manuscripts of Gustave Flaubert (1821- 1880), the famous French novelist reports about 62% accuracy when manuscripts are dated within a range of five calendar years with respect to their exact year of writing.

关键词： Testing Humans Calendars computer vision pattern recognition Databases Writing Watermarking Error analysis Libraries

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Irregular behavior recognition based on treading track

Irregular behavior recognition based on treading track

引用

International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Ye Zhang Zhi-Jing Liu School of computer science and technology Xidian University China

Visual analysis of human motion from video sequences is one of the most active research topics in the field of computer vision. This research has certain practical value and can be widely applied in some places such as: bank^ prison > courtroom, large public facility > large storehouse and military base .In this paper we present a novel method for judging irregular behavior based on treading track. First we use the background subtraction method to detect moving body, and then we judge whether someone is suspicious or not on the basis of treading track. The experimental result has shown the method present in this paper having the superiority of simple algorithm, fast recognition speed and high accuracy rate. Meanwhile, this method has certain robustness.

关键词： Object detection Motion detection Motion analysis Tracking Humans pattern analysis pattern recognition Image analysis Image sequence analysis Video sequences

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Hand Gesture recognition Using Fast Multi-scale Analysis

Hand Gesture Recognition Using Fast Multi-scale Analysis

引用

International Conference on Image and Graphics (ICIG)

作者： Yikai Fang Jian Cheng Kongqiao Wang Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Nokia Research Center Beijing China

Hand gesture has been used as a natural and efficient way in human computer interaction. Due to independence of auxiliary input devices, vision-based hand interfaces is more favorable for users. However, the process of hand gesture recognition is very time consuming, which often brings much frustration to users. In this paper, we propose a fast feature detection and description approach which can significantly speed up hand gesture recognition. Firstly, integral image is used to approximate Gaussian derivatives to calculate image convolution in feature detection. Then multi-scale geometric descriptors at feature points are obtained to represent hand gestures. Finally gesture is recognized with its geometric configuration. Experiments show that the proposed method needs much less time consumption while obtains comparative performance with its counterpart in literatures.

关键词： computer vision Convolution Human computer interaction Detectors pattern recognition Pervasive computing Boosting Computational efficiency Pixel Graphics

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Human body image segmentation based on wavelet analysis and active contour models

Human body image segmentation based on wavelet analysis and ...

引用

International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Jin-Yong Cheng Yi-Hui Liu School of Information Science and Technology Shandong Institute of Light Industry Jinan China

GVF Snake model is used widely in computer vision and image segmentation. However, there are problems in convergence processing to boundaries of human body image because of noise. This paper presents a new segmentation algorithm to human body image. First, rough edge is got by multi-scale algorithm of wavelet analysis, and then thinning method based on mathematical morphology is adopted to get edge map as foundation of GVF Snake model. This method solves the problem that the edge based on wavelets analysis is not consecutive. And it improves GVF Snake model's anti-noise ability. Experiments indicate that the new algorithm can improve snake model's ability to segment the complicated image.

关键词： Humans Image segmentation Wavelet analysis Image analysis Active contours Biological system modeling computer vision Convergence Algorithm design and analysis Morphology

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