7020261
Improved partial distance search for k nearest-neighbor classification
An important method in pattern recognition is k nearest-neighbor classification. However, its computational complexity limits its real-time applications. The partial distance search is a solution to this problem. Although it is not very effective, it can be combined with other algorithms to reduce the complexity. The paper proposes an indexing method that uses the variance vector of feature vectors of a design set to improve the efficiency of the partial distance search. Experimental results indicate the effectiveness of this indexing preprocessing
172381
Robust VQ-based digital watermarking for memoryless binary symmetric channel
Vector quantization (VQ) has been distinguished for its high compression rate in lossy data compression applications. And VQ-based watermarking plays a newly developed branch in digital watermarking research fields. In this paper, we propose optimized schemes for VQ-based image watermarking. We overcome the VQ index assignment problem with genetic algorithm, which is suitable for transmitting the watermarked image over noisy channels. We obtain better robustness of the watermarking algorithm against the effects caused by channel noise in our simulations after inspecting the results from several test images. In addition, to compare with existing schemes in literature, the watermarked image quality in our scheme has approximately the same quality, with better performance in robustness, to the schemes proposed by other researchers. This also proves the effectiveness of our proposed schemes in VQ-based image watermarking for copyright protection.
19822286
A Multi-user Based Watermarking System with Two-Security-Level Keys
This paper presents a new watermarking scheme to reduce the detection error probability through removal of local mean values of an original signal. It will be analytically proven that the removal process plays role in reducing the interference between an original signal and a watermark to be embedded. This is simply based on the orthogonality of the DC signal and the AC signal. As a result, the process improves the ability of the right detection. The proposed method is analytically and empirically evaluated with no attack as well as JPEG compression attacks.
33206794
Multipurpose image watermarking in DCT domain using subsampling
In this paper, a DCT-based multipurpose image watermarking algorithm is proposed. Through adopting dither modulation in subimages gained by subsampling, two independent robust watermarks can be embedded in the original image. Experimental results demonstrate the effectiveness of the proposed algorithm.
38537152
A Digital Image Watermarking Method Based on Labeled Bisecting Clustering Algorithm
13091217
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.
768133
Performance analysis of TCP-friendly AIMD algorithms for multimedia applications
In this paper, the performance of TCP-friendly generic AIMD (Additive Increase and Multiplicative Decrease) algorithms for Web-based playback and multirate multimedia applications is investigated. The necessary and sufficient TCP-friendly condition is derived, and the effectiveness and responsiveness of AIMD are studied. Due to practical implications, a Dynamic TCP-friendly AIMD (DTAIMD) algorithm is proposed. Extensive simulation results are given to verify the derived necessary and sufficient condition, and to demonstrate the performance of the proposed DTAIMD algorithm.
1422959
Reasoning Support for OWL-E
Syntax DL Syntax Semantics domain(u1, . . . , un) (u1, . . . , un) u D 1 × . . .× un
171830
Inconsistencies, Negations and Changes in Ontologies
Ontology management and maintenance are considered cornerstone issues in current Semantic Web applications in which semantic integration and ontological reasoning play a fundamental role. The ability to deal with inconsistency and to accommodate change is of utmost importance in real-world applications of ontological reasoning and management, wherein the need for expressing negated assertions also arises naturally. For this purpose, precise, formal definitions of the the different types of inconsistency and negation in ontologies are required. Unfortunately, ontology languages based on Description Logics (DLs) do not provide enough expressive power to represent axiom negations. Furthermore, there is no single, well-accepted notion of inconsistency and negation in the Semantic Web community, due to the lack of a common and solid foundational framework. In this paper, we propose a general framework accounting for inconsistency, negation and change in ontologies. Different levels of negation and inconsistency in DL-based ontologies are distinguished. We demonstrate how this framework can provide a foundation for reasoning with and management of dynamic ontologies.
2592435
Automatic image captioning
We examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.
6653256
Domain-constrained semi-supervised mining of tracking models in sensor networks
Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.