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1.
The technique of relevance feedback has been introduced to content-based 3D model retrieval, however, two essential issues
which affect the retrieval performance have not been addressed. In this paper, a novel relevance feedback mechanism is presented,
which effectively makes use of strengths of different feature vectors and perfectly solves the problem of small sample and
asymmetry. During the retrieval process, the proposed method takes the user’s feedback details as the relevant information
of query model, and then dynamically updates two important parameters of each feature vector, narrowing the gap between high-level
semantic knowledge and low-level object representation. The experiments, based on the publicly available 3D model database
Princeton Shape Benchmark (PSB), show that the proposed approach not only precisely captures the user’s semantic knowledge,
but also significantly improves the retrieval performance of 3D model retrieval. Compared with three state-of-the-art query
refinement schemes for 3D model retrieval, it provides superior retrieval effectiveness only with a few rounds of relevance
feedback based on several standard measures.
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3.
Traditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested.
However, the need exists for the development of category search for the retrieval of a specific category of music objects
which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic.
Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on
semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music
object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative
features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the
discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the
Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative
experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results
show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed
relevance feedback mechanism.
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4.
This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video
data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between
low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation
of the event classification and detection technique is presented and a future experiment to refine this technique is proposed.
These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation
scheme proposed in this paper.
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5.
In this paper, we propose an Interactive Object-based Image Clustering and Retrieval System (OCRS). The system incorporates
two major modules: Preprocessing and Object-based Image Retrieval. In preprocessing, an unsupervised segmentation method called
WavSeg is used to segment images into meaningful semantic regions (image objects). This is an area where a huge number of
image regions are involved. Therefore, we propose a Genetic Algorithm based algorithm to cluster these images objects and
thus reduce the search space for object-based image retrieval. In the learning and retrieval module, the Diverse Density algorithm
is adopted to analyze the user’s interest and generate the initial hypothesis which provides a prototype for future learning
and retrieval. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. In interacting
with user, we propose to use One-Class Support Vector Machine (SVM) to learn the user’s interest and refine the returned result.
Performance is evaluated on a large image database and the effectiveness of our retrieval algorithm is demonstrated through
comparative studies.
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6.
As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework. 相似文献
7.
In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories.
Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging
of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input
visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying
semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation
model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the
index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic
processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this
work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic
indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.
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8.
This paper describes an application of SVM (Support Vector Machines) to interactive document retrieval using active learning.
Some works have been done to apply classification learning like SVM to relevance feedback and have obtained successful results.
However they did not fully utilize characteristic of example distribution in document retrieval. We propose heuristics to
bias document showing for user’s judgement according to distribution of examples in document retrieval. This heuristics is
executed by selecting examples to show a user in neighbors of positive support vectors, and it improves learning efficiency.
We implemented a SVM-based interactive document retrieval system using our proposed heuristics, and compared it with conventional
systems like Rocchio-based system and a SVM-based system without the heuristics. We conducted systematic experiments using
large data sets including over 500,000 newspaper articles and confirmed our system outperformed other ones.
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9.
Relevance feedback is commonly incorporated into content-based image retrieval systems with the objective of improving retrieval
accuracy via user feedback. One effective method for improving retrieval performance is to perform feature re-weighting based
on the obtained feedback. Previous approaches to feature re-weighting via relevance feedback assume the feature data for images
can be represented in fixed-length vectors. However, many approaches are invalidated with the recent development of features
that cannot be represented in fixed-length vectors. In addition, previous approaches use only the information from the set
of images returned in the latest query result for feature re-weighting. In this paper, we propose a feature re-weighting approach
that places no restriction on the representation of feature data and utilizes the aggregate set of images returned over the
iterations of retrieval to obtain feature re-weighting information. The approach analyzes the feature distances calculated
between the query image and the resulting set of images to approximate the feature distances for the entire set of images
in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting.
There is no restriction on how the distances are calculated for each feature. This provides freedom for how the feature representations
are structured. The experimental results show the effectiveness of the proposed approach and in comparisons with other work,
it is shown that our approach outperforms previous work.
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10.
The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability
of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing
support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole.
A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular
scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards
increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings
partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable
behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.
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12.
Given its importance, the problem of object discovery in high-resolution remote-sensing (HRRS) imagery has received a lot
of attention in the literature. Despite the vast amount of expert endeavor spent on this problem, more efforts have been expected
to discover and utilize hidden semantics of images for object detection. To that end, in this paper, we address this problem
from two semantic perspectives. First, we propose a semantic-aware two-stage image segmentation approach, which preserves
the semantics of real-world objects during the segmentation process. Second, to better capture semantic features for object
discovery, we exploit a hyperclique pattern discovery method to find complex objects that consist of several co-existing individual
objects that usually form a unique semantic concept. We consider the identified groups of co-existing objects as new feature
sets and feed them into the learning model for better performance of image retrieval. Experiments with real-world datasets
show that, with reliable segmentation and new semantic features as starting points, we can improve the performance of object
discovery in terms of various external criteria.
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13.
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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14.
A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns
about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the
extent that these concerns are adequately addressed. In this article, we report on our work on P eopleF inder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends,
family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards
privacy as they interact with such an application, and to explore technologies that empower users to more effectively and
efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules
and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present
evaluations of these technologies in the context of one laboratory study and three field studies.
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16.
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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17.
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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18.
In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond
to a user’s query. Typically, the user has to refine the query many times and view query results until eventually the expected
videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates
the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing
imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence
of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video
databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each
of the two groups to be used in computing the relevance of each clip to the user’s query. In this paper, we provide an intelligent
query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting
sets is impossible or yields poor results.
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19.
Adaptive information filtering is a challenging and fascinating problem. It requires the adaptation of a representation of
a user’s multiple interests to various changes in them. We tackle this dynamic problem with Nootropia, a model inspired by
the autopoietic view of the immune system. It is based on a self-organising antibody network that reacts to user feedback
in order to define and preserve the user interests. We describe Nootropia in the context of adaptive, content-based document
filtering and evaluate it using virtual users. The results demonstrate Nootropia’s ability to adapt to both short-term variations
and more radical changes in the user’s interests, and to dynamically control its size and connectivity in the process. Advantages
over existing approaches to profile adaptation, such as learning algorithms and evolutionary algorithms are also highlighted.
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20.
In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems
is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance
of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different
information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine
their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with
crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement.
It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning
approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks.
The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational
efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.
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