Understanding and retrieving videos based on their semantic contents is an important research topic in multimedia datamining and has found various real-world applications. Most existing video analysis techniques focu...
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ISBN:
(纸本)9780769527017
Understanding and retrieving videos based on their semantic contents is an important research topic in multimedia datamining and has found various real-world applications. Most existing video analysis techniques focus on the low level visual features of video data. However, there is a "semantic gap" between the machine-readable features and the high level human concepts i.e. human understanding of the video content. In this paper, an interactive platform for semantic video mining and retrieval is proposed using Relevance Feedback (RF), a popular technique in the area of Content-based Image Retrieval (CBIR). By tracking semantic objects in a video and then modeling spatio-temporal events based on object trajectories and object interactions, the proposed interactive learning algorithm in the platform is able to mine the spatio-temporal data extracted from the video. An iterative learning process is involved in the proposed platform which is guided by the user's response to the retrieved results. Although the proposed video retrieval platform is intended for general use and can be tailored to many applications, we focus on its application in traffic surveillance video database retrieval to demonstrate the design details. the effectiveness of the algorithm is demonstrated by our experiments on real-life traffic surveillance videos.
Support vector machine (SVM) is a well-known method used for patternrecognition and machinelearning. However, training a SVM is very costly in terms of time and memory consumption when the data set is large. In cont...
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ISBN:
(纸本)3540354727
Support vector machine (SVM) is a well-known method used for patternrecognition and machinelearning. However, training a SVM is very costly in terms of time and memory consumption when the data set is large. In contrast, the SVM decision function is fully determined by a small subset of the training data, called support vectors. therefore, removing any training samples that are not relevant to support vectors might have no effect on building the proper decision function. In this paper, an effective hybrid method is proposed to remove from the training set the datathat is irrelevant to the final decision function, and thus the number of vectors for SVM training becomes small and the training time can be decreased greatly. Experimental results show that a significant amount of training data can be discarded by our methods without compromising the generalization capability of SVM.
作者：
He, JingYue, WuyiShi, YongKonan Univ
Inst Intelligent Informat & Commun Technol Kobe Hyogo 6588501 Japan Konan Univ
Dept Informat Sci & Syst Engn Kobe Hyogo 6588501 Japan Chinese Acad Sci
Res Ctr Data Technol & Knowledge Econ Beijing 100080 Peoples R China
this paper presents a datamining system of performance evaluation for multimedia communication networks (MCNs). Two important performance evaluation problems for the MCNs are considered in this paper. they are: (1) t...
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ISBN:
(纸本)3540343857
this paper presents a datamining system of performance evaluation for multimedia communication networks (MCNs). Two important performance evaluation problems for the MCNs are considered in this paper. they are: (1) the optimization problem for construction of the datamining system of performance evaluation;(2) the problem of categorizing real-time data corresponding to the datamining system by means of dividing the performance data into usual and unusual categories. An algorithm is employed to identify performance data such as throughput capacity, package forwarding rate, and response time. A software named PEDM2.0 (Performance Evaluation data Miner) is proposed to improve the accuracy and the effectiveness of the fuzzy linear programming (FLP) method compared with decision tree, neural network, and multiple criteria linear programming methods.
Given a large spatio-temporal database of events, where each event consists of the following fields: event-ID, time, location, event-type, mining spatio-temporal sequential patterns is to identify significant event ty...
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ISBN:
(纸本)9780898716115
Given a large spatio-temporal database of events, where each event consists of the following fields: event-ID, time, location, event-type, mining spatio-temporal sequential patterns is to identify significant event type sequences. Such spatio-temporal sequential patterns are crucial to investigate spatial and temporal evolutions of phenomena in many application domains. In this paper, we propose a sequence index as the significance measure for spatio-temporal sequential patterns, which is meaningful due to its interpretability using spatial statistics. We propose two algorithms, namely STS-Miner and Slicing-STS-Miner, to tackle the algorithmic design challenges under the spatial sequence index which does not preserve the downward closure property. We evaluate the algorithms by experimentally conducting performance evaluations using both synthetic and real world datasets.
there has been a lot of recent interest in miningpatterns from graphs. Often, the exact structure of the patterns of interest is not known. this happens, for example, when molecular structures are mined to discover f...
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ISBN:
(纸本)9780769527017
there has been a lot of recent interest in miningpatterns from graphs. Often, the exact structure of the patterns of interest is not known. this happens, for example, when molecular structures are mined to discover fragments useful as features in chemical compound classification task, or when web sites are mined to discover sets of web pages representing logical documents. Such patterns are often generated from a few small subgraphs (cores), according to certain generalization rules (GRs). We call such patterns "generalized patterns" (GPs). While being structurally different, GPs often perform the same function in the network. Previously proposed approaches to mining GPs either assumed that the cores and the GRs are given, or that all interesting GPs are frequent. these are strong assumptions, which often do not hold in practical applications. In this paper, we propose an approach to mining GPs that is free from the above assumptions. Given a small number of GPs selected by the user, our algorithm discovers all GPs similar to the user examples. First, a machinelearning-style approach is used to find the cores. Second, generalizations of the cores in the graph are computed to identify GPs. Evaluation on synthetic data, generated using real cores and GRs from biological and web domains, demonstrates effectiveness of our approach.
Knowledge representation is very important in intelligent systems - e.g. for knowledge discovery, datamining, and machinelearning. the human brain, a significant intelligent system, works with a huge number of spiki...
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ISBN:
(纸本)9781424400997
Knowledge representation is very important in intelligent systems - e.g. for knowledge discovery, datamining, and machinelearning. the human brain, a significant intelligent system, works with a huge number of spiking neurons. Based on spiking neuron models a new generation of spiking neural networks (SNNs) has been developed for artificial intelligence systems. SNNs are computationally more powerful than conventional artificial neural networks. In this paper, the spiking neuron model is applied to represent logic rules and fuzzy rules. Based on the STDP (Spike Timing Dependent Plasticity) principle, a new SNN model is proposed for patternrecognition. An efficient learning rule derived from the STDP is applied for self-organizing the input training set efficiently. An example, Animal-Growth-Record, is used to explain the principle of the SNN model. Benchmark data sets are applied to compare the proposed approach with other approaches. As there are very efficient learning rules in the SNN model, the model can be applied not only for fusion of multi-sensory data, but also for datamining in large databases with large numbers of attributes.
the proceedings contain 68 papers. the topics discussed include: a comparative analysis of data distribution methods in an agent-based neural system for classification tasks;stochastic differential portfolio games wit...
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ISBN:
(纸本)0769526624
the proceedings contain 68 papers. the topics discussed include: a comparative analysis of data distribution methods in an agent-based neural system for classification tasks;stochastic differential portfolio games with regime switching model;extracting symbolic rules from clustering of gene expression data;a novel microarray gene selection method based on consistency;combining greedy method and genetic algorithm to identify transcription factor binding sites;investigation of a new artificial immune system model applied to patternrecognition;RLM: a new method of encoding weights in DNA strands;shape representation and distance measure based on retational graph;fast modeling of curved object from two images;research on an improved gray gradient orientation algorithm in anisotropic high-pass filtering;and image color reduction based on self-organizing maps and growing self-organizing neural networks.
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples are known. In this paper, we propose an ...
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Clustering is one branch of unsupervised machinelearningtheory, which has a wide variety of applications in patternrecognition, image processing, economics, document categorization, web mining, etc. Today, we const...
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ISBN:
(纸本)3540465359
Clustering is one branch of unsupervised machinelearningtheory, which has a wide variety of applications in patternrecognition, image processing, economics, document categorization, web mining, etc. Today, we constantly face how to handle a large number of similar data items, which drives many researchers to contribute themselves to this field. Support vector machine provides a new pathway for clustering, however, it behaves bad in handling massive data. As an emergent theory, artificial immune system can effectively recognize antigens and produce the memory antibodies. this mechanism is constantly used to achieve representative or feature data from raw data. A combinational clustering method is proposed in this paper based on artificial immune system and support vector machine, Experimentation in functionality and performance is done in detail. Finally a more challenging application in elevator industry is conducted. the results strongly indicate that this combinational clustering in this paper is of feasibility and of practice.
A conceptual clustering program CLUSTER3 is described that, given a set of objects represented by attribute-value tuples, groups them into clusters described by generalized conjunctive descriptions in attributional ca...
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ISBN:
(纸本)1845641787
A conceptual clustering program CLUSTER3 is described that, given a set of objects represented by attribute-value tuples, groups them into clusters described by generalized conjunctive descriptions in attributional calculus. the descriptions are optimized according to a user-designed multi-criterion clustering quality measure. the clustering process in CLUSTER3 depends on a viewpoint underlying the clustering goal, and employs the view-relevant attribute subsetting method (VAS) that selects for clustering only attributes relevant to this viewpoint. the program is illustrated by a simple designed problem and by its application to clustering of US Congressional voting records. the ongoing research concerns application of CLUSTER3 to large and complex datasets such as collections of web pages.
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