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International Symposium on Intelligent Multimedia, Video and Speech Processing

作者： Zhi-Qiang Liu School of Creative Media City University of Hong Kong Hong Kong China Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineerin University of Melbourne VIC Australia

We present the adaptive manifold self-organising map (AMSOM) for a face retrieval system. Our experimental results show that it has an excellent potential for face retrieval applications. As compared to the more tradi... 详细信息

ISBN: (纸本)9628576623

关键词： Face recognition Neurons Face detection Vectors Gabor filters Lattices computer vision Feature extraction Data mining Detectors

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A framework for object-based image retrieval at the semantic level 3rd

A framework for object-based image retrieval at the semantic...

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3rd International Conference on Visual Information Systems, VISUAL 1999

作者： Jia, Linhui Kitchen, Leslie Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineering Melbourne University ParkvilleVIC3052 Australia

This paper proposes a framework with essential components and processes for object-based image retrieval based on semantically meaningful classes of objects in images. An instantiation of the framework is presented to... 详细信息

ISBN: (纸本)3540660798

关键词： Semantics

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Classification-driven object-based image retrieval

Classification-driven object-based image retrieval

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： L. Jia L. Kitchen Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia

This paper describes an approach for object-based image retrieval based on classes of objects in images. In this approach, contours of objects are extracted from images and are represented under a scheme which satisfies scale, rotation and translation invariance. Classifier learning techniques are used to classify objects in images into different classes. Image similarity calculation is performed based on class information of objects. Experimental results show that the method is effective and efficient.

关键词： Image retrieval Shape Image databases Image segmentation Classification tree analysis Object recognition Decision trees computer vision Machine intelligence computer science

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PREFACE

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International Journal of Pattern Recognition and Artificial intelligence 1999年第5期13卷 589-590页

作者： MOHAMED CHERIET YEE-HONG YANG Laboratoire d'Imagerie de Vision et d'Intelligence Artificielle École de Technologie Supérieure 1100 rue Notre-Dame Ouest Montréal (Québec) H3C 1K3 Canada SAM (Scene Analysis and Modelling Group) Computer Vision and Graphics Lab Department of Computer Science University of Saskatchewan 57 Campus Drive Saskatoon (Saskatchewan) S7N 5A9 Canada

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Learning fuzzy modelling through genetic algorithm for object recognition

Learning fuzzy modelling through genetic algorithm for objec...

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IEEE International Conference on Evolutionary Computation

作者： R. Soodamani Z.Q. Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Australia

The paper proposes a genetic-algorithm-based learning strategy that models membership functions of the fuzzy attributes of surfaces in a model based machine vision system. The objective function aims at enhancing recognition performance in terms of maximizing the degree of discrimination among classes. As a result, the accuracy of recognizing known instances of objects and generalisation capability by recognizing unknown instances of known objects are greatly improved. The performance enhancement of a model based object recognition system consisting of a set of synthetic range images is established by incorporating a dynamic off-line learning mechanism using a genetic algorithm in the feedback path of the system.

关键词： Genetic algorithms Object recognition Fuzzy systems Fuzzy sets Shape Feedback System testing Image segmentation computer vision Machine intelligence

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A technique for 3D object representation and detection in knowledge-based and content-driven image retrieval

A technique for 3D object representation and detection in kn...

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： Linhui Jia Zhi-Qiang Liu Computer Vision and hIachine Intelligence Lab Department of Computer Science University of Melbourne Parkville VIC Australia

ISBN: (纸本)0780342534

The paper proposes a 3D object structure representation and detection scheme for object-based image retrieval. Based on findings in psychological research on visual cognition, this scheme utilizes qualitative components of a 3D object in an image as the basis for object representation, and performs partial object matching for object detection. During this process, the contextual information is used. This technique plays an important role in the 3D object-based image retrieval system under development.

关键词： Object detection Image retrieval Psychology Cognition

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A knowledge based approach to shape from shading

A knowledge based approach to shape from shading

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： N. Barnes Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Parkville VIC Australia

We study the problem of recovering the approximate three-dimensional shape of an object when knowledge about the object is available. The application of knowledge-based methods to image processing tasks will help overcome problems which arise from processing images using a pixel-based approach. We show that by applying domain specific knowledge, we can fully automate the derivation of the approximate shape of an object. Further, this approach can yield specific advantages over existing approaches, both in terms of computation and processing results. This is a powerful paradigm that will have applications in object recognition, robotic navigation, and domain specific scene understanding.

关键词： Shape Image processing Pixel Object recognition Robotics and automation Navigation Layout

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Markov random field models for handwritten word recognition

Markov random field models for handwritten word recognition

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： Jinhai Cai Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Parkville VIC Australia

ISBN: (纸本)0780342534

We propose the use of a Markov random field model for handwritten word recognition. The main advantage of Markov random field models is that they provide flexible and natural models for the interaction between spatially related random variables in their neighborhood systems via clique functions. In our scheme, Gabor filters are adopted for feature extraction. A fuzzy neighborhood system is proposed and fuzzy matching measurements are developed to cope with the variability of handwritten word shapes. A relaxation labeling algorithm is used to maximize the global compatibilities of Markov random fields. The influence of neighborhood sizes and the iteration number on recognition rates of the system is investigated. Our initial experiments have shown encouraging results.

关键词： Markov random fields Handwriting recognition Fuzzy systems Shape measurement Random variables Gabor filters Feature extraction labeling

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Improving linear separability of classes via feature projection

Improving linear separability of classes via feature project...

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IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： V. Chandrasekaran Zhi-Qiang Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Carlton VIC Australia

It is well known that a linearly separable set of classes is ideal for a pattern recognition task. The majority of pattern recognition research has been devoted to achieve linear separability of classes by nonlinear input-output mapping. We develop a novel idea of class label separation by projecting each element of the feature vector onto a manifold. The functional characteristics of the manifold associated with each feature type are learnt iteratively from the class label distribution under an optimization criterion. This process attempts to transform an n-dimensional nonlinearly separable feature classification task to an n-dimensional linearly separable problem. The burden of classifying features that are associated with multiple class labels is handled by projections of other discriminating features. This enables fast learning of the classification task by the second stage network which accepts the projected output as its input. If the classification task is modified by an addition of a feature element, the system requires iterative learning of the manifold associated with this new unit only and does not require learning of the whole set of features as seen in conventional neural networks. This iterative knowledge aggregation permits ease of fine tuning and selection of an optimal set of parameters for a given task. The above concept is demonstrated on a set of classification tasks.

关键词： Pattern recognition Vectors Neural networks

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A context-driven intelligent database processing system using object-oriented fuzzy cognitive maps

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 1996年第9期11卷 671-689页

作者： Satur, R Liu, ZQ CVMIL—Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Victoria Australia

Most data sets that describe and evolve from real-world systems are by nature semiquantitative or qualitative rather than quantitative. This can mean large variations in the significance of results that are derived from this data for decision-making processes given that the original database provides training and prototypical examples that reflect systems of events in the real world. In this article we propose a structure for a Knowledge-Based System (KBS) that is derived using significance within given contextual domains. Data that would ordinarily be classified by simple attribute classification techniques are now categorized by understanding patterns and value distributions for attributes and attribute domains that exist within rich and dense databases such as in the case of census databases double dagger and Geographic Information Systems (GIS);rich by the very number of fields and interpretations, depending on the context in which the data are to be reviewed. The structure we have implemented for capturing and structuring semiquantitative information is the Fuzzy Cognitive Map (FCM). We also reduce the number of false patterns labeled ''significant'' by incorporating the knowledge used by human experts to find significance within the data. We treat this knowledge as initial background knowledge and as a minimal set for distinguishing significance for particular attribute values within a given context. (C) 1996 John Wiley & Sons, Inc.

关键词： Knowledge based systems

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