A new dual wing harmonium model for document retrieval

A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.     

Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning

Finite mixture is widely used in the fields of information processing and data analysis. However, its model selection, i.e., the selection of components in the mixture for a given sample data set, has been still a rather difficult task. Recently, the Bayesian Ying-Yang (BYY) harmony learning has provided a new approach to the Gaussian mixture modeling with a favorite feature that model selection can be made automatically during parameter learning. In this paper, based on the same BYY harmony learning framework for finite mixture, we propose an adaptive gradient BYY learning algorithm for Poisson mixture with automated model selection. It is demonstrated well by the simulation experiments that this adaptive gradient BYY learning algorithm can automatically determine the number of actual Poisson components for a sample data set, with a good estimation of the parameters in the original or true mixture where the components are separated in a certain degree. Moreover, the adaptive gradient BYY learning algorithm is successfully applied to texture classification.     

3D noisy discrete objects: Segmentation and application to smoothing

We propose in this paper a segmentation process that can deal with noisy discrete objects. A flexible approach considering arithmetic discrete planes with a variable width is used to avoid the over-segmentation that might happen when classical segmentation algorithms based on regular discrete planes are used to decompose the surface of the object. A method to choose a seed and different segmentation strategies according to the shape of the surface is also proposed, as well as an application to smooth the border of convex noisy discrete objects.     

Stable local dimensionality reduction approaches

Dimensionality reduction is a big challenge in many areas. A large number of local approaches, stemming from statistics or geometry, have been developed. However, in practice these local approaches are often in lack of robustness, since in contrast to maximum variance unfolding (MVU), which explicitly unfolds the manifold, they merely characterize local geometry structure. Moreover, the eigenproblems that they encounter, are hard to solve. We propose a unified framework that explicitly unfolds the manifold and reformulate local approaches as the semi-definite programs instead of the above-mentioned eigenproblems. Three well-known algorithms, locally linear embedding (LLE), laplacian eigenmaps (LE) and local tangent space alignment (LTSA) are reinterpreted and improved within this framework. Several experiments are presented to demonstrate the potential of our framework and the improvements of these local algorithms.     

Computing upper and lower bounds of rotation angles from digital images

Rotations in the discrete plane are important for many applications such as image matching or construction of mosaic images. We suppose that a digital image A is transformed to another digital image B by a rotation. In the discrete plane, there are many angles giving the rotation from A to B, which we call admissible rotation angles from A to B. For such a set of admissible rotation angles, there exist two angles that achieve the lower and the upper bounds. To find those lower and upper bounds, we use hinge angles as used in Nouvel and Rémila [Incremental and transitive discrete rotations, in: R. Reulke, U. Eckardt, B. Flash, U. Knauer, K. Polthier (Eds.), Combinatorial Image Analysis, Lecture Notes in Computer Science, vol. 4040, Springer, Berlin, 2006, pp. 199-213]. A sequence of hinge angles is a set of particular angles determined by a digital image in the sense that any angle between two consecutive hinge angles gives the identical rotation of the digital image. We propose a method for obtaining the lower and the upper bounds of admissible rotation angles using hinge angles from a given Euclidean angle or from a pair of corresponding digital images.     

Combining appearance and motion for face and gender recognition from videos

While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. Inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. Our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos.     

Induction of multiclass multifeature split decision trees from distributed data

The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one such method that generates compact trees using multifeature splits in place of single feature split decision trees generated by most existing methods for distributed data. Our method is based on Fisher's linear discriminant function, and is capable of dealing with multiple classes in the data. For homogeneously distributed data, the decision trees produced by our method are identical to decision trees generated using Fisher's linear discriminant function with centrally stored data. For heterogeneously distributed data, a certain approximation is involved with a small change in performance with respect to the tree generated with centrally stored data. Experimental results for several well-known datasets are presented and compared with decision trees generated using Fisher's linear discriminant function with centrally stored data.     

Two efficient two-factor authenticated key exchange protocols in public wireless LANs

Recently, Parks et al. proposed an authentication and key agreement protocol for low-power PDAs in public wireless LANs using two factors including a password and a token, e.g. a smart card. They claimed that their proposed scheme provided mutual authentication, identity privacy, half-forward secrecy and low computation cost for a client including just one symmetric key encryption and five hash operations. In this paper, we point out that Park et al.’s protocol is vulnerable to the dictionary attack upon identity privacy. We propose two schemes with mutual authentication, half-forward secrecy, lower computation cost and less exchanged messages than Park et al.’s protocol. In additional to these properties, identity privacy, which is not satisfied by Park et al.’s protocol, is also provided by our second scheme.     

Hardware assisted watermarking for multimedia

Digital media offer several distinct advantages over analog media, such as high quality, ease of editing, and ease of processing operations such as compression and high fidelity copying. Digital data is commonly available through digital TV broadcast, CD, DVD, and computing devices such as personal computers. The ease by which a digital media object can be duplicated and distributed has led to the need for effective digital rights management tools. Digital watermarking is one such tool. Watermarking is the process of embedding extra data called a watermark into a multimedia object, like image, audio, or video, such that the watermark can later be detected or extracted in order to make an assertion regarding the object. During the last decade, numerous software based watermarking schemes have appeared in the literature and watermarking research has attained a certain degree of maturity. But hardware based watermarking systems have evolved more recently only and they are still at their infancy. The goal of hardware assisted watermarking is to achieve low power usage, real-time performance, reliability, and ease of integration with existing consumer electronic devices. In this paper, we survey the hardware assisted solutions proposed in the literature for watermarking of multimedia objects. The survey is preceded by an introduction to the background issues involved in digital watermarking.     

Parallel crypto-devices for GF(p) elliptic curve multiplication resistant against side channel attacks

All elliptic curve cryptographic schemes are based on scalar multiplication of points, and hence its faster computation signifies faster operation. This paper proposes two different parallelization techniques to speedup the GF(p) elliptic curve multiplication in affine coordinates and the corresponding architectures. The proposed implementations are capable of resisting different side channel attacks based on time and power analysis. The 160, 192, 224 and 256 bits implementations of both the architectures have been synthesized and simulated for both FPGA and 0.13μ CMOS ASIC. The final designs have been prototyped on a Xilinx Virtex-4 xc4vlx200-12ff1513 FPGA board and performance analyzes carried out. The experimental result and performance comparison show better throughput of the proposed implementations as compared to existing reported architectures.