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learning in content based image retrieval - a brief review

Learning in content based image retrieval - a brief review

引用

international conference on Information, Communications and signal processing

作者： Yan Gao Kap Luk Chan Wei-Yun Yau School of Electrical and Electronic Engineering Nanyang Technological University Singapore IPTV Programme Institute for Infocomm Research Singapore

ISBN: (纸本)9781424409822;1424409829

In recent works on content based image retrieval, machine learning has been playing an even more important role. this is motivated by the need to bridge the semantic gap between the low-level visual features and the high-level human perception. this paper presents a review of the latest development in learning methodologies applied to CBIR. It is reviewed from three aspects: discriminative classification, generative modeling and similarity learning. through this review, we observe the trends in learning for CBIR and conclude with future research directions.

关键词： Image retrieval Content based retrieval Humans Feedback machine learning Bridges Linear discriminant analysis Support vector machines Boosting IPTV

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A cooperative learning strategy for interactive video search

A cooperative learning strategy for interactive video search

引用

international conference on Information, Communications and signal processing

作者： Shikui Wei Zhenfeng Zhu Yao Zhao Nan Liu Institute of Information Science Beijing Jiaotong University Beijing China National Key Laboratory on Machine Perception Beijing University Beijing China

ISBN: (纸本)9781424409822

the goal of this paper is to develop a learning strategy for interactive video search that can effectively mitigate the burden on users without decreasing search performance. Taking SVM as underlying learner, a cooperative training strategy is proposed for learning a ranking function, in which semi-supervised learning procedure is started with a combination of a few positive training seeds and a relative large set of unlabeled data. the main merit of the proposed framework is its ability to mine automatically training samples from previous answer set and to refine gradually ranking model during cooperative training phase. In addition, as an extension of the proposed framework, multiple modalities can be potentially combined for effectively learning user's query intention. Following the guideline of TRECVID' 06 video search task, we validate the effectiveness of our proposed method.

关键词： Support vector machines Labeling Supervised learning Search engines machine learning Feedback Semisupervised learning Statistics Information science Laboratories

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SVM-Based decision fusion model for detecting concepts in films

SVM-Based decision fusion model for detecting concepts in fi...

引用

international conference on Information, Communications and signal processing

作者： P. Muneesawang L. Guan Department of Electrical and Computer Engineering Naresuan University Phitsanulok Thailand Department of Electrical and Computer Engineering Ryerson University Toronto Canada

this paper studies a support vector machine (SVM) to obtain a decision fusion algorithm for detection of semantic concepts in videos, and its application to films database. Given a movie clip, its spatio-temporal information is captured by audiovisual features. these are then independently inputted to the corresponding matching experts whose outputs are fused at the decision stage by the SVM classifier. Based on our simulation results, this fusion method can attain very high recognition accuracy for detection of various concepts from a collection of Hollywood movies. It requires a very small set of training samples from a large database.

关键词： Motion pictures Support vector machines signal processing algorithms Data mining Event detection machine learning Spatial databases Support vector machine classification Information retrieval Indexing

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Deriving configuration knowledge and evaluating product variants through intelligent techniques

Deriving configuration knowledge and evaluating product vari...

引用

international conference on Information, Communications and signal processing

作者： Haifeng Liu Youliang Huang Wee-Keong Ng Bin Song Xiang Li Wen-Feng Lu School of Computer Engineering Nanyang Technological University Singapore Singapore Institute of Manufacturing Technology Singapore Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (纸本)9781424409822;1424409829

Mass customization has become a crucial business strategy for product manufacturers that aims at satisfying individual customer needs with near mass production efficiency. Companies must develop the necessary infrastructure to derive valid product configurations that satisfy the requirements of lifecycle cost along with customer’s constraints. In this paper, to overcome the drawback of current product configurators, we apply a rule mining approach to automatically generate configuration knowledge, and present a hybrid approach based on Activity Based Costing (ABC) and machine learning techniques to estimate LCC of derived product variants from a constraintbased configurator at the design stage. the proposed intelligent techniques would benefit companies in enhancing product development capability in a shorter lifecycle.

关键词： Mass customization Manufacturing Mass production Companies Costs Hybrid power systems Costing machine learning learning systems Product development

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the Berlin Brain-Computer Interface (BBCI) -: towards a new communication channel for online control in gaming applications

The Berlin Brain-Computer Interface (BBCI) -: towards a new ...

引用

9th international conference on Distributed Multimedia Systems (DMS 03)/6th international conference on Visual Information Systems (VIS 2003)

作者： Krepki, Roman Blankertz, Benjamin Curio, Gabriel Mueller, Klaus-Robert Fraunhofer Inst Comp Architecture & Software Tech Res Grp Intelligent Data Anal D-61462 Koenigstein I Ts Germany Free Univ Berlin Klinikum Benjamin Franklin Dept Neurol Neurophys Grp D-12203 Berlin Germany Univ Potsdam Dept Comp Sci D-14482 Potsdam Germany

the investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future multimedia research and development. Brain-Computer Interfaces (BCI) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e. g., a dexterity game, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in electroencephalographic (EEG) signals which are recorded non-invasively from user's scalp. this contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents setups where the user is provided with intuitive control strategies in plausible gaming applications that use biofeedback. Yet at its beginning, BBCI thus adds a new dimension in multimedia research by offering the user an additional and independent communication channel based on brain activity only. First successful experiments already yielded inspiring proofs-of-concept. A diversity of multimedia application models, say computer games, and their specific intuitive control strategies, as well as various Virtual Reality (VR) scenarios are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates.

关键词： brain-computer interface electroencephalography digital signal processing machine learning biofeedback human-computer interaction brain-gaming

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Internetworking security model based on intelligent system

Internetworking security model based on intelligent system

引用

international conference on Information, Communications and signal processing

作者： Surat Srinoy Witcha Chimphlee Department of Computer Science Suan Dusit Rajabhat University Bangkok Thailand

ISBN: (纸本)9781424409822

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. In this paper we present the architecture of our system that combines anomaly and misuse intrusion detection in a hybrid system that tries to take benefit of the best practices of both misuse and anomaly detection approaches. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods.

关键词： Internetworking Intelligent systems Intrusion detection Computer security Computer networks Computer science Electronic mail Information systems machine learning Data mining

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Shot Boundary Detection by a Hierarchical Supervised Approach

Shot Boundary Detection by a Hierarchical Supervised Approac...

引用

international conference on Systems, signals and Image processing, IWSSIP

作者： G. Camara-Chavez F. Precioso M. Cord S. Phillip-Foliguet A. de A. Araujo Equipe Traitement des Images et du Signal ENSEA Cergy-Pontoise France DCC Minas Gerais Brazil

Video shot boundary detection plays an important role in video processing. It is the first step toward video-content analysis and content-based video retrieval. We develop a hierarchical approach for shot boundary detection based on the assumption that hierarchy helps to take decisions by reducing the amount of indeterminate transitions. Our method consists in first detecting abrupt transitions using a learning-based approach, then non-abrupt transitions are split into gradual transitions and normal frames. We describe in this paper, a machine learning system for shot boundary detection. the core of this system is a kernel-based SVM classifier. We present some results obtained for shot extraction TRECVID 2006 Task.

关键词： Gunshot detection systems Support vector machines Support vector machine classification Histograms Lighting Data mining Motion detection Brightness Content based retrieval Video sequences

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Acoustic Features Extraction for Emotion Recognition

Acoustic Features Extraction for Emotion Recognition

引用

international conference on Computer and Information Science (ACIS)

作者： Jia Rong Yi-Ping Phoebe Chen Morshed Chowdhury Gang Li School of Engineering and Information Technology Deakin University Melbourne Australia

In the last decade, the efforts of spoken language processing have achieved significant advances, however, the work with emotional recognition has not progressed so far, and can only achieve 50% to 60% in accuracy. this is because a majority of researchers in this field have focused on the synthesis of emotional speech rather than focusing on automating human emotion recognition. Many research groups have focused on how to improve the performance of the classifier they used for emotion recognition, and few work has been done on data pre-processing, such as the extraction and selection of a set of specifying acoustic features instead of using all the possible ones they had in hand. To work with well-selected acoustic features does not mean to delay the whole job, but this will save much time and resources by removing the irrelative information and reducing the high-dimension data calculation. In this paper, we developed an automatic feature selector based on a RF2TREE algorithm and the traditional C4.5 algorithm. RF2TREE applied here helped us to solve the problems that did not have enough data examples. the ensemble learning technique was applied to enlarge the original data set by building a bagged random forest to generate many virtual examples, and then the new data set was used to train a single decision tree, which selects the most efficient features to represent the speech signals for the emotion recognition. Finally, the output of the selector was a set of specifying acoustic features, produced by RF2TREE and a single decision tree.

关键词： Feature extraction Emotion recognition Psychology machine learning Decision trees Speech synthesis Humans Data mining signal processing algorithms Speech recognition

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Extracting room acoustic parameters from music

Extracting room acoustic parameters from music

引用

6th international conference on Auditorium Acoustics 2006

作者： Cox, T.J. Kendrick, P. Li, F.F. Chambers, J. Zhang, Y. Acoustics Research Centre University of Salford Salford M5 4WT United Kingdom Centre of Digital Signal Processing Cardiff School of Engineering Cardiff University Cardiff CF24 0YF United Kingdom Department of Computing and Mathematics Manchester Metropolitan University Manchester M1 5GD United Kingdom

ISBN: (纸本)9781604235869

Using music as a test signal enables the acoustic parameters of auditoria to be measured, in use, without disturbing users. However, music is not an ideal signal due to its non-stationary characteristics and uneven excitation across the octave band being analysed. In recent years, several methods have been developed for measuring room acoustic parameters using naturally occurring sounds. Currently, the most useful appear to be either based on experiential learning using machine learning techniques or on a maximum likelihood approach. So far, these methods have been restricted to examining the reverberance of the space either through the EDT or RT. this paper will contrast these methods and examine how successful they are at predicting parameters which relate to clarity and strength.

关键词： Maximum likelihood

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A new architecture for deriving dynamic brain-machine interfaces

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6th international conference on Computational Science (ICCS 2006)

作者： Fortes, Jose Figueiredo, Renato Hermer-Vazquez, Linda Principe, Jose Sanchez, Justin C. Univ Florida Dep Elect & Comp Engn Gainesville FL 32611 USA Univ Florida Dept Psychol Gainesville FL 32611 USA Univ Florida Dept Pediat Gainesville FL 32611 USA

ISBN: (纸本)3540343830

Great potential exists for future Brain machine Interfaces (BMIs) to help paralyzed patients, and others with motor disabilities, regain (artificial) motor control and autonomy. this paper describes a novel approach towards the development of new design architectures and research test-beds for advanced BMIs. It addresses a critical design challenge in deriving the functional mapping between the subject's movement intent and actuated behavior. Currently, adaptive signal processing techniques are used to correlate neuronal modulation with known movements generated by the subject. However, with patients who are paralyzed, access to the individual's movement is unavailable. Inspired by motor control research, this paper considers a predictive framework for BMI using multiple adaptive models trained with supervised or reinforcement learning in a closed-loop architecture that requires real-time feedback. Here, movement trajectories can be inferred and incrementally updated using instantaneous knowledge of the movement target and the individual's current neuronal activation. In this framework, BMIs require a computing infrastructure capable of selectively executing multiple models on the basis of signals received by and/or provided to the brain in real time. Middleware currently under investigation to provide this data-driven dynamic capability is discussed.

关键词： Biomedical engineering

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