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1.
The audio channel conveys rich clues for content-based multimedia indexing. Interesting audio analysis includes, besides widely
known speech recognition and speaker identification problems, speech/music segmentation, speaker gender detection, special
effect recognition such as gun shots or car pursuit, and so on. All these problems can be considered as an audio classification
problem which needs to generate a label from low audio signal analysis. While most audio analysis techniques in the literature
are problem specific, we propose in this paper a general framework for audio classification. The proposed technique uses a
perceptually motivated model of the human perception of audio classes in the sense that it makes a judicious use of certain
psychophysical results and relies on a neural network for classification. In order to assess the effectiveness of the proposed
approach, large experiments on several audio classification problems have been carried out, including speech/music discrimination
in Radio/TV programs, gender recognition on a subset of the switchboard database, highlights detection in sports videos, and
musical genre recognition. The classification accuracies of the proposed technique are comparable to those obtained by problem
specific techniques while offering the basis of a general approach for audio classification.
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2.
Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of
mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed.
First, we proposed an efficient online algorithm, FTP-stream ( Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence
representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list
structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments
show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and
just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change
( changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody
structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst,
negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody
structures over continuous music query streams.
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3.
As the growing in Internet, database types and sizes are getting various and larger. The topic of finding out the significant
information from a database at the shortest time is important. In the music databases, a repeating pattern is an important
feature of music objects, which commonly used in analyzing the repeated part of music data and looking for themes. Most of
the repeating patterns are key melodies or easy to familiarize and remember for people. Therefore, we can use the themes or
the repeating patterns to construct indices that can speedup query execution for music retrievals. Nevertheless, non-trivial
repeating patterns exclude those patterns, which are all contained in other longer patterns, such that they can reduce the
redundancy of the repeating patterns and save the index space needed. Most of existing algorithms are time consuming for finding
non-trivial repeating patterns in a music object. In this research, we aim to apply the true suffix tree approach to discover
non-trivial repeating patterns for a music object, which can efficiently address the cost problems in processing time and
memory space. In general case, our proposed scheme can extract non-trivial repeating patterns in a linear time.
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4.
Querying live media streams is a challenging problem that is becoming an essential requirement in a growing number of applications.
Research in multimedia information systems has addressed and made good progress in dealing with archived data. Meanwhile,
research in stream databases has received significant attention for querying alphanumeric symbolic streams. The lack of a
data model capable of representing different multimedia data in a declarative way, hiding the media heterogeneity and providing
reasonable abstractions for querying live multimedia streams poses the challenge of how to make the best use of data in video,
audio and other media sources for various applications. In this paper we propose a system that enables directly capturing
media streams from sensors and automatically generating more meaningful feature streams that can be queried by a data stream
processor. The system provides an effective combination between extendible digital processing techniques and general data
stream management research. Together with other query techniques developed in related data stream management streams, our
system can be used in those application areas where multifarious live media senors are deployed for surveillance, disaster
response, live conferencing, telepresence, etc.
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5.
A new method for data hiding in H.264/AVC streams is presented. The proposed method exploits the IPCM encoded macroblocks
during the intra prediction stage in order to hide the desired data. It is a blind data hiding scheme, i.e. the message can
be extracted directly from the encoded stream without the need of the original host video. Moreover, the method exhibits the
useful property of reusing the compressed stream for hiding different data numerous times without considerably affecting either
the bit-rate or the perceptual quality. This property allows data hiding directly in the compressed stream in real time. The
method perfectly suits to covert communication and content authentication applications .
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6.
This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is
presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length
repeating patterns using an “aggressive” accession during searching, by avoiding costly repetition frequency calculation and
by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental
results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.
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7.
Three dimensional human motions recorded by motion capture and hand gestures recorded by using data gloves generate variable-length
data streams. These data streams usually have dozens of attributes, and have different variations for similar motions. To
segment and recognize motion streams, a classification-based approach is proposed in this paper. Classification feature vectors
are extracted by utilizing singular value decompositions (SVD) of motion data. The extracted feature vectors capture the dominating
geometric structures of motion data as revealed by SVD. Multi-class support vector machine (SVM) classifiers with class probability
estimates are explored for classifying the feature vectors in order to segment and recognize motion streams. Experiments show
that the proposed approach can find patterns in motion data streams with high accuracy.
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8.
We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence
of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming
and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its
direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.
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9.
We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed
about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked
on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive,
although they had several important reservations about camera-phone technology more generally. Data from our pointing-device
task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device
trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings,
design lessons for camera-phone and visual-tag applications are presented.
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10.
Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for
exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying
the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation
that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective
feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation
and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite
effectively navigate complicated routes.
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11.
This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary
Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition
and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational
intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process,
we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.
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12.
This paper proposes camera and media stream management techniques at the middleware level for implementing a U-City (ubiquitous
city). The study focuses on overcoming the difficulties associated with developing middleware capable of processing and streaming
multimedia data from a large number of cameras by expanding the traditional media processing technology. The content of the
study can be classified into two main categories: One is a camera array management technique that involves the middleware-level
framework and protocol for managing the camera array. The other is the media stream management technique for effective delivery
management and processing of the multimedia streams from the camera array.
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13.
Applying video smoothing techniques to real-time video transmission can significantly reduce the peak rate and rate variability
of compressed video streams. Moreover, statistical multiplexing of the smoothed traffic can substantially improve network
utilization. In this paper we propose a new smoothing scheme, which exploits statistical multiplexing gain that can be obtained
after smoothing of individual video streams. We present a new bandwidth allocation algorithm that allows for responsive interactivity.
The local re-smoothing algorithm is carried out using an iterative process. In the proposed scheme the smoothed video streams
are divided into fixed intervals and then a new transmission schedule for each interval is calculated. The problem of applying
an optimal transmission schedule for aggregated smoothing video streams is shown to be NP-hard problem. Partitioning the whole
stream into sections enables parallel processing of the smoothing algorithm in real-time before transmission. This approach
allows partial transmission of the multiplexed stream while smoothing other intervals. The simulation results show a significant
reduction in peak rate and rate variability of the aggregated stream, compared to the non-smoothing case. Therefore the proposed
scheme allows us to increase the number of simultanusally-served video streams.
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14.
Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed. 相似文献
15.
In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence
of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in
families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology
we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls
to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction
with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls
in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities
show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness
performances efficiently.
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16.
Television daily produces massive amounts of videos. Digital video is unfortunately an unstructured document in which it is
very difficult to find any information. Television streams have however a strong and stable but hidden structure that we want
to discover by detecting repeating objects in the video stream. This paper shows that television streams are actually highly
redundant and that detecting repeats can be an effective way to detect the underlying structure of the video. A method for
detecting these repetitions is presented here with an emphasis on the efficiency of the search in a large video corpus. Very
good results are obtained both in terms of effectiveness (98% in recall and precision) as well as efficiency since one day
of video is queried against a 3 weeks dataset in only 1 s.
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17.
In order to be capable of exploiting context for pro-active information recommendation, agents need to extract and understand
user activities based on their knowledge of the user interests. In this paper, we propose a novel approach for context-aware
recommendation in browsing assistants based on the integration of user profiles, navigational patterns and contextual elements.
In this approach, user profiles built using an unsupervised Web page clustering algorithm are used to characterize user ongoing
activities and behavior patterns. Experimental evidence show that using longer-term interests to explain active browsing goals
user assistance is effectively enhanced.
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18.
The purposes of this study are (a) to establish a measurement for evaluating conversational impressions of group discussions,
and (b) to make an exploratory investigation on their interactional processes which may affect to form those impressions.
The impression rating and factor analysis undertaken first give us four factors concerning conversational impressions of “focus
group interviews (FGIs)”: conversational activeness, conversational sequencing, the attitudes of participants and the relationships
of participants. In relation to the factors of conversational activeness and conversational sequencing in particular, the
microanalysis of four selected topical scenes from our database further shows that the behavior of the moderator and the interviewees
is organized not independently but with reference to each other. The study thus emphasizes the importance of the integration
of quantitative and qualitative approaches towards human interactions.
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19.
This paper describes security and privacy issues for multimedia database management systems. Multimedia data includes text,
images, audio and video. It describes access control for multimedia database management systems and describes security policies
and security architectures for such systems. Privacy problems that result from multimedia data mining are also discussed.
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20.
Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these
algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive
information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one
to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods,
instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving
clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general
features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better
protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated.
It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well
as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to
which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements.
In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then,
we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations
about future work and promising directions in the context of privacy preservation in data mining are discussed.
*The work reported in this paper has been partially supported by the EU under the IST Project CODMINE and by the Sponsors of
CERIAS.
Editor: Geoff Webb
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