A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN

Video semantic detection has been one research hotspot in the field of human-computer interaction. In video features-oriented sparse representation, the features from the same category video could not achieve similar coding results. To address this, the Locality-Sensitive Discriminant Sparse Representation (LSDSR) is developed, in order that the video samples belonging to the same video category are encoded as similar sparse codes which make them have better category discrimination. In the LSDSR, a discriminative loss function based on sparse coefficients is imposed on the locality-sensitive sparse representation, which makes the optimized dictionary for sparse representation be discriminative. The LSDSR for video features enhances the power of semantic discrimination to optimize the dictionary and build the better discriminant sparse model. More so, to further improve the accuracy of video semantic detection after sparse representation, a weighted K-Nearest Neighbor (KNN) classification method with the loss function that integrates reconstruction error and discrimination for the sparse representation is adopted to detect video semantic concepts. The proposed methods are evaluated on the related video databases in comparison with existing sparse representation methods. The experimental results show that the proposed methods significantly enhance the power of discrimination of video features, and consequently improve the accuracy of video semantic concept detection.     

基于修改核函数的RLS－SVM多用户检测算法

为了解决支持向量机算法在多用户检测中存在的模型复杂及产生的支持向量数目较多的问题,该文提出一种新的非线性多用户检测算法。该算法在第一次小样本训练时引入了遗忘因子,该因子使支持向量数减少了28％。在第一次训练后产生的支持向量的基础上,将黎曼几何结构引入到输入空间,利用黎曼几何结构将分类器中的核函数进行修改,在第二次训练中再次减少了支持向量数目。此方法在牺牲较少误比特率的基础上,简化了算法模型和降低计算复杂度。仿真实验表明,该算法抑制了多径引起的码间干扰,性能接近于最优多用户检测器。     

L0-norm sparse representation based on modified genetic algorithm for face recognition

The typical sparse representation for classification (SRC) exploits the training samples to represent the test samples, and classifies the test samples based on the representation results. SRC is essentially an L₀-norm minimization problem which can theoretically yield the sparsest representation and lead to the promising classification performance. We know that it is difficult to directly resolve L₀-norm minimization problem by applying usual optimization method. To effectively address this problem, we propose the L₀-norm based SRC by exploiting a modified genetic algorithm (GA), termed GASRC, in this paper. The basic idea of GASRC is that it modifies the traditional genetic algorithm and then uses the modified GA (MGA) to select a part of the training samples to represent a test sample. Compared with the conventional SRC based on L₁-norm optimization, GASRC can achieve better classification performance. Experiments on several popular real-world databases show the good classification effectiveness of our approach.     

基于ARM9的网络视频监控系统设计与实现

针对传统监控系统无法联网、维护困难、成本高等不足,运用嵌入式技术和图像处理技术,设计了一种基于ARM9的网络视频监控系统。系统在Linux平台下利用Video4Linux进行视频图像采集,并进行JPEG压缩编码,通过构建嵌入式Web服务器,结合Java Applet技术,实现了远程网络用户通过IE浏览器对现场的视频监控。试验测试结果表明:在图像分辨率为640×480时,传输速率达到了20帧每秒。系统运行稳定、画面流畅、成本较低,具有广泛的应用价值。     

Design of sparse array for mad imaging based on maximizing information capacity

In past years, growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array. An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible, meanwhile without deteriorating imaging quality. For this end, we propose a novel methodology of arranging sensors in an optimal manner, exploring the concept of information capacity developed originally in the communication society. The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem, which can be resolved efficiently using widely adopted Sinmltaneous Perturbation and Statistical Algorithm （SPSA）. Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.