6401713
Stability Analysis of Regional and National Voting Schemes by a Continuous Model
The previous discrete-model-based stability analysis of regional and national voting has been extended to a continuous-model-based analysis in the simultaneous presence of white and concentrated components of noise, reconfirming the previous conclusion that regional voting with smaller sized regions always demonstrates an improved stability over those with larger sized regions, including the national voting in its limiting case in particular. The conclusion remains valid as long as the weak distribution assumption is valid.
9936649
A differential LSI method for document classification
We have developed an effective probabilistic classifier for document classification by introducing the concept of the differential document vectors and DLSI (differential latent semantics index) spaces. A simple posteriori calculation using the intraand extra-document statistics demonstrates the advantage of the DLSI space-based probabilistic classifier over the popularly used LSI space-based classifier in classification performance.
13763556
A new scheme for an automatic generation of multi-variable fuzzy systems
Abstract We present a new novel method of automatically generating a multi-variable fuzzy inference system from the given sample sets. We first decompose the sample set, say Λ , into a cluster of sample sets associated with the given input variables, then compute the associated fuzzy rules and membership functions for each variable, independent of the other variables, by solving a single input multiple output fuzzy system extracted from the set cluster. The resulting decomposed fuzzy rules and membership functions for all the variables are integrated back into the fuzzy system appropriate for the original sample set Λ . Taking advantage of the independence of the input variables in computing the decomposed systems, we show that the computational complexity of the multi-variable system can in principle be reduced to that of a single variable if we can use a parallel processing multi-CPU system. We have verified our claim using an eight variable nonlinear function.
13758632
A new differential LSI space-based probabilistic document classifier
We have developed a new effective probabilistic classifier for document classification by introducing the con differential document vectors and DLSI (differential latent semantic indexing) spaces. A combined use of the projection the distances to the DLSI spaces introduced from the differential document vectors improves the adaptability of the LS semantic indexing) method by capturing unique characteristics of documents. Using the intraand extra-document both a simple posteriori calculation on a small example and an experiment on a large Reuters-21578 database demo advantage of the DLSI space-based probabilistic classifier over the LSI space-based classifier in classification perform  2003 Elsevier B.V. All rights reserved.
3257950
Robustness of Regional Matching Scheme over Global Matching Scheme
Analyzing the effect of concentrated noise on a typical decision-making process of a simplified two-candidate voting model, we have demonstrated that a local approach using a regional matching process is more robust and stable than a direct approach using a global matching process, by establishing that the former is capable of accommodating a higher level of noise than the latter before the result of the decision overturns. To extend the theory to imagery analysis, we pose a conjecture that our conclusion on the robustness of the regional matching processes remains valid not only for the simpler vote counting schemes but also for practically more important decision-making schemes in image analysis which involve dimension-reducing transforms or other features extraction processes such as principal component analysis or Gablor transforms. Two convincing experimental verifications are provided, supporting not only the theory by a white-black flag recognition problem on a pixel-by-pixel basis, but also the validity of the conjecture by a facial recognition problem in the presence of localized noise typically represented by clutter or occlusion in imagery.
16327059
Globally synchronized dead-reckoning with local lag for continuous distributed multiplayer games
Dead-Reckoning (DR) is an effective method to maintain consistency for Continuous Distributed Multiplayer Games (CDMG). Since DR can filter most unnecessary state updates and improve the scalability of a system, it is widely used in commercial CDMG. However, DR cannot maintain high consistency, and this constrains its application in highly interactive games. With the help of global synchronization, DR can achieve higher consistency, but it still cannot eliminate before inconsistency. In this paper, a method named Globally Synchronized DR with Local Lag (GS-DR-LL), which combines local lag and Globally Synchronized DR (GS-DR), is presented. Performance evaluation shows that GS-DR-LL can effectively decrease before inconsistency, and the effects increase with the lag.
2498239
Automatic threshold decision of background registration technique for video segmentation
Background registration technique is useful to solve still object problem and uncovered background problem for video segmentation. However, it is hard to automatically decide the threshold of the frame difference for background registration to make it more feasible for real-time applications. Many previous works made efforts on automatic threshold decision for change detection. In this paper, we propose a new method of automatic threshold decision algorithm in a totally different viewpoint. Not only change detection but also the quantization effect in discrete domain is concerned. A Gaussianity test is first applied to find the standard deviation of Gaussian noise from the camera. Then, the quantization effect in discrete domain is taken into consideration to derive the relation between the standard deviation and the optimal threshold value. A couple MPEG-4 sequences and experimental sequences are tested as examples. Simulation results show that the calculated threshold values are suitable for background registration to give good segmentation results.
4181067
Error-propagation analysis and concealment strategy for MPEG-4 video bitstream with data partitioning
Data partitioning, one of the MPEG-4 video error resilience tools, enables better error robustness. However, it suffers from serious error propagation problem. In this paper, we use an experimental approach to model the error propagation with several commonly used error detection conditions. It is shown that errors detected in forward section of texture data may be propagated from motion data, while those in DCT coefficients mostly result from themselves. Furthermore, the motion marker is the major error source for several error conditions detected in motion part. According to these characteristics, motion marker assumption and backtracking-based concealment strategies are proposed to achieve more accurate error localization in bitstream domain.
5161241
Protein structure alignment by deterministic annealing
MOTIVATION
Protein structure alignment is one of the most important computational problems in molecular biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and so on. From the viewpoint of computational complexity, a pairwise structure alignment is also a NP-hard problem, in contrast to the polynomial time algorithm for a pairwise sequence alignment.


RESULTS
We propose a method for solving the structure alignment problem in an accurate manner at the amino acid level, based on a mean field annealing technique. We define the structure alignment as a mixed integer-programming (MIP) problem. By avoiding complicated combinatorial computation and exploiting the special structure of the continuous partial problem, we transform the MIP into a reduced non-linear continuous optimization problem (NCOP) with a much simpler form. To optimize the reduced NCOP, a mean field annealing procedure is adopted with a modified Potts model, whose solution is generally identical to that of the MIP. There is no 'soft constraint' in our mean field model and all constraints are automatically satisfied throughout the annealing process, thereby not only making the optimization more efficient but also eliminating many unnecessary parameters that depend on problems and usually require careful tuning. A number of benchmark examples are tested by the proposed method with comparisons to several existing approaches.
32640232
AP-Based Borda Voting Method for Feature Extraction in TRECVID-2004
We present a novel fusion method — AP-based Borda voting method (APBB)— for rankings. Due to its adaptive weighting scheme, APBB outperforms many traditional methods. Comparative experiments on TRECVID 2004 data were carried out and showed the robustness and effectiveness of this method.