10472812
Building concept ontology for medical video annotation
Most existing systems for content-based video retrieval (CBVR) are now amenable to support automatic low-level video content analysis and feature extraction, but they have limited effectiveness from a user's perspective. To support semantic video retrieval via keywords, we have proposed a novel framework by incorporating the concept ontology to enable more effective modeling and representation of semantic video concepts. Specifically, this novel framework includes: (a) Using the salient objects to achieve a middle-level understanding of the semantics of video contents; (b) Building a domain dependent concept ontology to enable multi-level modeling and representation of semantic video concepts; (c) Developing a multi-task boosting technique to achieve hierarchical video classifier training for automatic multi-level video annotation. The experimental results in a certain domain of medical education videos are also provided.
147992
A hierarchical access control model for video database systems
Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.
800122
Concept-oriented video skimming via semantic video classification
Effective video skimming requires a good understanding of the semantics of video contents. However, more existing systems for content-based video retrieval (CBVR) can only support low-level video analysis, but they have limited effectiveness on achieving semantic-sensitive video understanding. In this paper, we have developed a novel framework to achieve concept-oriented video skimming and it consists of three parts: (a) using salient objects for semantic-sensitive video content representation; (b) using finite mixture models for semantic video concept modeling and classification; (c) enabling concept-oriented video skimming via semantic video classification.
10379627
Medical video mining for efficient database indexing, management and access
To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.
5651829
Utilizing Domain Knowledge in Neuroevolution
We propose a method called Rule-based ESP (RESP) for utilizing prior knowledge evolving Artificial Neural Networks (ANNs). First, KBANN-likete chniques are used to transform a set of rules into an ANN, then the ANN is trained using the Enforced Subpopulations (ESP) neuroevolution method. Empirical results in the Prey Capture domain show that RESP can reach higher level of performance than ESP. The results also suggest that incremental learning is not necessary with RESP, and it is often easier to design a set of rules than an incremental evolution scheme. In addition, an experiment with some of the rules deleted suggests that RESP is robust even with an incomplete knowledge base. RESP therefore provides a robust methodology for scaling up neuroevolution to harder tasks by utilizing existing knowledge about the domain.
12382521
On the design and performance of prefix-preserving IP traffic trace anonymization
Even though real-world Internet traffic traces are crucial for network research, only a tiny percentage of traffic traces collected are made public. One major reason why traffic trace owners hesitate to make the traces publicly available is the concern that the confidential and private information may be inferred from the trace. In this paper we focus on the problem of anonymizing IP addresses in a trace. More specifically, we are interested in prefixpreserving anonymization in which the prefix relationship among IP addresses is preserved in the anonymized trace, making such a trace usable in situations where such prefix relationships are important. The goal of our work is two fold. First, we are interested in analyzing the security properties inherent in prefix-preserving IP address anonymization. Through the analysis of IP traffic traces, we investigate the effect of some types of attacks on the security of the prefix-preserving anonymization process. We also derive results for the optimum manner in which an attack should proceed which provides a bound on the performance of attacks in general. Second, we observe that an existing scheme used for prefix-preserving anonymization, TCPdpriv, has some drawbacks that limit its use in a large-scale, distributed setting. We develop an alternative cryptography-based, prefixpreserving anonymization technique to address these drawbacks while maintaining the same level of anonymity as TCPdpriv.
11303603
Wavelet deconvolution
This paper studies the issue of optimal deconvolution density estimation using wavelets. The approach taken here can be considered as orthogonal series estimation in the more general context of the density estimation. We explore the asymptotic properties of estimators based on thresholding of estimated wavelet coefficients. Minimax rates of convergence under the integrated square loss are studied over Besov classes B/sub /spl sigma/pq/ of functions for both ordinary smooth and supersmooth convolution kernels. The minimax rates of convergence depend on the smoothness of functions to be deconvolved and the decay rate of the characteristic function of convolution kernels. It is shown that no linear deconvolution estimators can achieve the optimal rates of convergence in the Besov spaces with p<2 when the convolution kernel is ordinary smooth and super smooth. If the convolution kernel is ordinary smooth, then linear estimators can be improved by using thresholding wavelet deconvolution estimators which are asymptotically minimax within logarithmic terms. Adaptive minimax properties of thresholding wavelet deconvolution estimators are also discussed.
228217
Localized on-chip power delivery network optimization via sequence of linear programming
In this paper, we propose an efficient algorithm to reduce the voltage noises for on-chip power/ground (P/G) networks of VLSI. The new method is based on the sequence of linear programming (SLP) method as the optimization engine and a localized scheme via partitioning for dealing with large circuits. We show that by directly optimizing the decap area as the objective function and using the time-domain adjoint method, SLP can deliver much better quality than existing methods based on the merged time-domain adjoint method. The partitioning strategy further improves the scalability of the proposed algorithm and makes it efficient for large circuits. The resulting algorithm is general enough for any P/G network. Experimental results demonstrate the advantage of the proposed method over existing state-of-the-art methods in terms of solution quality at a mild computation cost increase
9912670
On the Quadratic Convergence of the Levenberg-Marquardt Method without Nonsingularity Assumption
Recently, Yamashita and Fukushima [11] established an interesting quadratic convergence result for the Levenberg-Marquardt method without the nonsingularity assumption. This paper extends the result of Yamashita and Fukushima by using μk=||F(xk)||δ, where δ∈[1,2], instead of μk=||F(xk)||2 as the Levenberg-Marquardt parameter. If ||F(x)|| provides a local error bound for the system of nonlinear equations F(x)=0, it is shown that the sequence {xk} generated by the new method converges to a solution quadratically, which is stronger than dist(xk,X*)→0 given by Yamashita and Fukushima. Numerical results show that the method performs well for singular problems.