120181133
Face recognition based on discriminative manifold learning
In this paper, a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the recently proposed LLE, Isomap and Eigenmap algorithms, which are based on reconstruction purpose, our method uses the RCA algorithm to achieve nonlinear embedding and data discrimination at the same time. Also, the LLE and Isomap algorithms are crucially depends on the appropriateness of the neighborhood construction rule, in this paper, a CK-nearest neighborhood rule is proposed to achieve better neighborhood construction. Experimental results indicate the promising performance of the proposed method.
15085849
A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.
7983053
Auto-Calibration of a Compound-Type Omnidirectional Camera
We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.
34620301
Video Local Pattern based Image Matching for Visual Mapping
Image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. Based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visually-similar and temporally-adjacent to that frame. Instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. A model is then estimated from a local pattern, and the matching of images is performed by comparing these models. Experimental results demonstrated its improvement over that comparing individual frames
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
17672081
A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components are weighted to reflect the high-level concepts, and a user's subjective perception, embodied in the images labelled by the user in the feedback. However, the number of labelled images is often small and the covariance matrix needed for weighting will be singular. For this reason, the commonly used methods discard the mutual correlation among the feature components completely and use a diagonal covariance matrix. In this paper, a sub-vector weighting scheme is proposed. This scheme partitions a multi-dimensional visual feature vector into multiple low-dimensional sub-vectors. The singularity of the covariance matrix for each sub-vector can be avoided due to the lower dimensionality of the sub-vectors. Thus, the mutual correlation in each sub-vector can be retained for weighting and an optimally weighted similarity metric can be applied on each sub-vector. The similarity scores obtained from different sub-vectors are combined, as the final scor...
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance
32146489
Cycle Period Analysis and Optimization of Timed Circuits
In this paper, a method is proposed to analyze the minimum average cycle period of the timed circuits. Timed Petri net is used to model timed circuits. Our method is focus on structural analysis of the Petri net model of the timed circuits, which is another way to reduce the state space of the analyzed model. Then an algorithm is proposed to optimize the performance of timed circuit by asynchronous retiming technique. The algorithm balances the asynchronous pipelines to gain the target cycle period while minimize the area at the same time. Experimental results demonstrate the computational feasibility and effectiveness of both approaches.
31834649
A Novel Artificial Immune Network Model and Analysis on Its Dynamic Behavior and Stabilities
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. Simulation results show that the limit cycle and chaos may exist simultaneously when four units are in connection, and the network's characteristic has a close relationship with the intensity of suppressor T-cell's function, B-cell's characteristics and transconductance. Besides this, with Liapunov's method, the sufficient conditions for network's stability is studied, especially for the case of system's characteristics under the condition that the helper T-cells appear as a nonlinear function.
14541123
An evolutionary algorithm with population immunity and its application on autonomous robot control
The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.
201804
Analysis on Appearance Reasons of the Positive Feedback Traders
Positive feedback traders buy the asset when price is rising and sell it when price is falling. This type of speculation in short term causes the violent fluctuation for a given asset price, and make great influence to the stabilization of the financial market. The price reaction model under the influence of noise traders is established by considering the influence which the investment cost and information arrival rate make to the arbitrage and asset price. We analyze the reasons and conditions why positive feedback traders are born from quantitative analysis