The existing Krawtchouk moment invariants are derived by a linear combination of geometric moment invariants. This indirect method cannot achieve perfect performance in rotation, scale, and translation (RST) invariant image recognition since the derivation of these invariants are not built on Krawtchouk polynomials. A direct method to derive RST invariants from Krawtchouk moments, named explicit Krawtchouk moment invariants, is proposed. The proposed method drives Krawtchouk moment invariants by algebraically eliminating the distorted (i.e., rotated, scaled, and translated) factor contained in the Krawtchouk moments of distorted image. Experimental results show that, compared with the indirect methods, the proposed approach can significantly improve the performance in terms of recognition accuracy and noise robustness.
Blue noise sampling is a core component for a large number of computer graphic applications such as imaging, modeling, animation, and rendering. However, most existing methods are concentrated on preserving spatial domain properties like density and anisotropy, while ignoring feature preserving. In order to solve the problem, we present a new distance metric called mixture distance for blue noise sampling, which is a combination of geodesic and feature distances. Based on mixture distance, the blue noise property and features can be preserved by controlling the ratio of the geodesic distance to the feature distance. With the intention of meeting different requirements from various applications, an adaptive adjustment for parameters is also proposed to achieve a balance between the preservation of features and spatial properties. Finally, implementation on a graphic processing unit is introduced to improve the efficiency of computation. The efficacy of the method is demonstrated by the results of image stippling, surface sampling, and remeshing.
The classical circularity measure that considers the relationship between the shape area and the shape perimeter is sensitive to noise and limited for shapes with boundary defects (which lead to a large increase in perimeter). We propose a recent and generic method for shape circularity measure. The proposed method derives from radial moments and satisfies the desirable properties of circularity measures. Compared with the classical circularity measure and Hu moment invariants based method, the new measure is robust to noise and performs better in the case of shapes with boundary defects. Experimental results also show the superiority of the proposed method.
Digital forensics investigator faces the challenge of reliability of forensic conclusions. Formal automatic analysis method
is helpful to deal with the challenge. The finite state machine analysis method tries to determine all possible sequences of
events that could have happened in a digital system during an incident. Its basic idea is to model the target system using
a finite state machine and then explore its all possible states on the condition of available evidence. Timed mealy finite
state machine is introduced to model the target system, and the formalization of system running process and evidence is
presented to match the system running with possible source evidence automatically. Based on Gladyshev's basic
reasoning method, general reasoning algorithms with multi strategies are developed to find the possible real scenarios.
Case study and experimental results show that our method is feasible and adaptable to possible cases and takes a further
step to practical formal reasoning for digital forensics.
Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both
regarded as emotional information which is playing an important role in human-computer interaction. Based on our
previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this
paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules
include speech enhancement for eliminating noises, rapid face detection for locating face from background image,
example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature
tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier.
Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10
facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected
due to the synchronization when speech and video fused together. The experiment results have demonstrated that this
system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules
fused recognition will become the trend of emotion recognition in the future.
Proc. SPIE. 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006
KEYWORDS: Principal component analysis, Detection and tracking algorithms, Data modeling, Sensors, Error analysis, Data processing, Computer intrusion detection, Reconstruction algorithms, Systems modeling, Network security
Intrusion Detection Systems (IDSs) need a mass of labeled data in the process of training, which hampers the application and popularity of traditional IDSs. Classical principal component analysis is highly sensitive to outliers in training data, and leads to poor classification accuracy. This paper proposes a novel scheme based on robust principal component classifier, which obtains principal components that are not influenced much by outliers. An anomaly detection model is constructed from the distances in the principal component space and the reconstruction error of training data. The experiments show that this proposed approach can detect unknown intrusions effectively, and has a good performance in detection rate and false positive rate especially.
Practical Intrusion Detection Systems (IDSs) based on data mining are facing two key problems, discovering intrusion knowledge from real-time network data, and automatically updating them when new intrusions appear. Most data mining algorithms work on labeled data. In order to set up basic data set for mining, huge volumes of network data need to be collected and labeled manually. In fact, it is rather difficult and impractical to label intrusions, which has been a big restrict for current IDSs and has led to limited ability of identifying all kinds of intrusion types. An improved unsupervised clustering-based intrusion model working on unlabeled training data is introduced. In this model, center of a cluster is defined and used as substitution of this cluster. Then all cluster centers are adopted to detect intrusions. Testing on data sets of KDDCUP’99, experimental results demonstrate that our method has good performance in detection rate. Furthermore, the incremental-learning method is adopted to detect those unknown-type intrusions and it decreases false positive rate.
There are already some extensions of rough set theory for incomplete information systems, such as tolerance relation, limited tolerance relation, similarity relation, and etc. But there are no approaches and algorithms for these extensions. A direct approach for processing incomplete information systems is developed in this paper, including discretization, attribute reduction, value reduction, and rule matching. This approach can be used in all kinds of extensions of rough set theory for incomplete information systems. It is both effective in complete and incomplete information systems.
Intrusion detection is an essential component of critical infrastructure protection mechanism. Since many current IDSs are constructed by manual encoding of expert knowledge, it is time-consuming to update their knowledge. In order to solve this problem, an effective method for misuse intrusion detection with low cost and high efficiency is presented. This paper gives an overview of our research in building a detection model for identifying known intrusions, their variations and novel attacks with unknown natures. The method is based on rough set theory and capable of extracting a set of detection rules from network packet features. After getting a decision table through preprocessing raw packet data, rough-set-based reduction and rule generation algorithms are applied, and useful rules for intrusion detection are obtained. In addition, a rough set and rule-tree-based incremental knowledge acquisition algorithm is presented in order to solve problems of updating rule set when new attacks appear. Compared with other methods, our method requires a smaller size of training data set and less effort to collect training data. Experimental results demonstrate that our system is effective and more suitable for online intrusion detection.
Rough set is a valid mathematical theory developed in recent years. It has the ability to deal with imprecise, uncertain, and vague information. It has been applied in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring successfully. In this paper, we will make a comparative study of the algebra view and information view of rough set theory. Some inequivalent relationships between these two views of rough set theory in inconsistent decision table systems are discovered. It corrects an error of many researchers, that is, the algebra view and information view of rough set theory are equivalent. It is helpful for developing heuristic knowledge reduction algorithms for inconsistent decision table systems.
One main technical means of anti-Spam is to build filters in email transfer route. However, the design of many junk mail filters hasn't made use of the whole security information in an email, which exists mostly in mail header rather than in the text and accessory. In this paper, data mining based on rough sets is introduced to design a new anti-Spam filter. Firstly, by recording and analyzing the header of every collected email sample, we get all necessary original raw data. Next, by selecting and computing features from the original header data, we obtain our decision table including several condition attributes and one decision attribute. Then, a data mining technique based on rough sets, which mainly includes relative reduction and rule generation, is introduced to mine this decision table. And we obtain some useful anti-Spam knowledge from all the email headers. Finally, we have made tests by using our rules to judge different mails. Tests demonstrate that when mining on selected baleful email corpus with specific Spam rate, our anti-Spam filter has high efficiency and high identification rate. By mining email headers, we can find potential security problems of some email systems and cheating methods of Spam senders.
Rough set theory is emerging as a new tool for dealing with fuzzy and uncertain data. In this paper, a theory is developed to express, measure and process uncertain information and uncertain knowledge based on our result about the uncertainty measure of decision tables and decision rule systems. Based on Skowron’s propositional default rule generation algorithm, we develop an initiative learning model with rough set based initiative rule generation algorithm. Simulation results illustrate its efficiency.
Rough Set is a valid mathematical theory developed in recent years, which has the ability to deal with imprecise, uncertain, and vague information. It has been applied in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring successfully. Many researchers have studied rough sets in different view. In this paper, the authors discuss the reduction of knowledge using information entropy in rough set theory. First, the changing tendency of the conditional entropy of decision attributes given condition attributes is studied from the viewpoint of information. Then, two new algorithms based on conditional entropy are developed. These two algorithms are analyzed and compared with MIBARK algorithm. Furthermore, our simulation results show that the algorithms can find the minimal reduction in most cases.
Wavelet transforms via lifting scheme are called the second-generation wavelet transforms. However, in some lifting schemes the coefficients are transformed using mathematical method from the first-generation wavelets, so the filters with better performance using in lifting are limited. The spatial structures of lifting scheme are also simple. For example, the classical lifting scheme, predicting-updating, is two-stage, and most researchers simply adopt this structure. In addition, in most design results the lifting filters are not only hard to get and also fixed. In our former work, we had presented a new three-stage lifting scheme, predicting-updating-adapting, and the results of filter design are no more fixed. In this paper, we continue to research the spatial model of lifting scheme. A group of general multi-stage lifting schemes are achieved and designed. All lifting filters are designed in spatial domain and proper mathematical methods are selected. Our designed coefficients are flexible and can be adjusted according to different data. We give the mathematical design details in this paper. Finally, all designed model of lifting are used in image compression and satisfactory results are achieved.
Rough Set is a valid mathematical theory developed in recent years, which has the ability to deal with imprecise, uncertain, and vague information. It has been applied in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring successfully. Many researchers have studied rough sets in different view. In this paper, the algebra view and information view of rough set theory are analyzed and compared with each other systematically. Some equivalence relations and other kind of relations such as inclusion relations are resulted through comparing study. For example, the reduction under algebra view will be equivalent to the reduction under information view if the decision table is consistent. Otherwise, the reduction under information view will include the reduction under algebra view. These results will be useful for designing heuristic reduction algorithms.
Aiming at the demand of adaptive wavelet transforms via lifting, a three-stage lifting scheme (predict-update-adapt) is proposed according to common two-stage lifting scheme (predict-update) in this paper. The second stage is updating stage. The third is adaptive predicting stage. Our scheme is an update-then-predict scheme that can detect jumps in image from the updated data and it needs not any more additional information. The first stage is the key in our scheme. It is the interim of updating. Its coefficient can be adjusted to adapt to data to achieve a better result. In the adaptive predicting stage, we use symmetric prediction filters in the smooth area of image, while asymmetric prediction filters at the edge of jumps to reduce predicting errors. We design these filters using spatial method directly. The inherent relationships between the coefficients of the first stage and the other stages are found and presented by equations. Thus, the design result is a class of filters with coefficient that are no longer invariant. Simulation result of image coding with our scheme is good.
In this paper, we discuss the design of an MPEG-4 compression technology based multimedia e-mail system. As our test in ISDN context indicates, the MPEG-4 gives high compression ratio up to 130:1 without discernable quality deterioration in multimedia e-mail application where video is featured by infrequent and slow motions. It is an exciting improvement over other compressors in this area. The performance comparison of our system with other similar systems is also given in this paper. IN our multimedia e- mail system, messaging subsystem is implemented with Messaging Application Programming Interface (MAPI). A special architecture of multimedia e-mail package is also presented.
In this paper, we propose an approach that can generate logical rules from an information system. It is based on Pawlak's rough set theory. There are two steps in our rule generation approach. First, attribute reduction is done on an information table according to Skowron's discernibility matrix and logic function simplification, some important and valuable attributes are extracted. Then, value reduction is performed and corresponding logic rules are generated. All reducts including the minimal reduct of an information system can be obtained through these two reductions. Our approach can generate both the maximal generalized decision rules as well as potential interesting and useful rules according to requirements.
As the amount of information in the world is steadily increasing, there is a growing demand for tools for analyzing the information. Many scholars have been working hard to study machine learning in order to obtain knowledge from domain data sets. They hope to find patterns in terms of implicit dependencies in data. Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Some scholars have done much work to interpret neural networks so that they will no longer be seen as black boxes and provided some plots and methods for knowledge acquisition using neural networks. These can be classified into three categories: fuzzy neural networks, CF (certainty factor) based neural networks, and logical neurons. We review some of these research works in this paper.