6736183
Feature selection based on maximizing separability in Gauss mixture model and its application to image classification
We propose a feature selection algorithm suitable for classification problems. Our algorithm tries to find a subset of features, which maximizes separability between Gaussian clusters. To reduce the complexity of exhaustive searching the best feature set, we follow a backward elimination method. Our feature selection algorithm can be applied to a full search classifier to obtain a single global subspace. However, one global subspace may not alone capture local behavior well. We realize multiple subspace clustering by applying our dimension reduction algorithm to a tree structured classifier. Experimental results show that the resulting classifier not only removes irrelevant features but also improves classification performance.
6861234
Vector quantization with model selection
We propose an iterative algorithm that incorporates model selection into entropy-constrained vector quantization. Two model selection steps are added to the classic Lloyd algorithm as additional necessary conditions for optimality. Codewords are pruned by using a Lagrangian with entropy and codebook size constraints. Relevant features are found by using a partitioned vector quantization. Relevant and irrelevant features are modelled independently. Moreover, we model irrelevant features by a global probability density function to make them independent of partition cells. This enables us to avoid a problem in comparing the performances of vector quantizers in different dimensional spaces. As a Lagrangian decreases, we not only obtain a locally optimal codebook, but also reduce codebook size and identify relevant features.
17166398
Image classification using GMM with context information and with a solution of singular covariance problem
Summary form only given. Taking the average of feature vectors from the center and neighboring blocks to a block being coded is proposed as a method of considering context information in block classification. The algorithm has the advantage of low complexity. Gauss mixture models (GMM) are adopted to extract features from image blocks, including an algorithm to handle singular covariance matrices. Two different distortion measures are used; namely log-likelihood quadratic discrimination analysis (QDA) and a dimension-compensated distortion measure defined by dividing the QDA distortion by the corresponding cell's dimension. Aerial images were used to train and test. Experimental results show that the proposed algorithm not only improves the classification performance, but also provides a solution to the singular covariance problem.
14200816
Context-dependent tree-structured image classification using the QDA distortion measure and the hidden Markov model
Vector quantization based on the Gauss mixture model (GMM) and the quadratic discriminant analysis (QDA) distortion measure has been shown to perform well in statistical image classification problems. Previous work in this area has concentrated on designing a separate GMM-based vector quantizer using the QDA distortion measure for each class using full search. We design a single vector quantizer for all classes using a tree-structured algorithm based on the (generalized) BFOS algorithm. This reduces the search complexity, while it increases the correct classification rate. Further, the pruning stage of our algorithm takes into account the dependencies between the image blocks assuming a hidden Markov model (HMM). During the test stage, our algorithm aims to iteratively maximize the joint probability of occurrence of all image blocks based on the HMM. Our simulation results indicate that our algorithm performs better (both in terms of computational complexity and classification rate) when compared to the previously published algorithms based on the GMM.
3134846
Tiny-sync: Tight time synchronization for wireless sensor networks
Time synchronization is a fundamental middleware service for any distributed system. Wireless sensor networks make extensive use of synchronized time in many contexts (e.g., data fusion, TDMA schedules, synchronized sleep periods, etc.). We propose a time synchronization method relevant for wireless sensor networks. The solution features minimal complexity in network bandwidth, storage as well as processing, and can achieve good accuracy. Especially relevant for sensor networks, it also provides tight, deterministic bounds on offset and clock drift. A method for synchronizing the entire network is presented. The performance of the algorithm is analyzed theoretically and validated on a realistic testbed. The results show that the proposed algorithm outperforms existing algorithms in terms of precision and resource requirements.
2770616
Learning Domain-Specific Control Knowledge from Random Walks
We describe and evaluate a system for learning domain-specific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well on "long random walk" problem distributions. The system is based on viewing planning domains as very large Markov decision processes and then applying a recent variant of approximate policy iteration that is bootstrapped with a new technique based on random walks. We evaluate the system on the AIPS-2000 planning domains (among others) and show that often the learned policies perform well on problems drawn from the long–random-walk distribution. In addition, we show that these policies often perform well on the original problem distributions from the domains involved. Our evaluation also uncovers limitations of our current system that point to future challenges.
479580
The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks
Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (CAG) algorithm that forms clusters of nodes sensing similar values within a given threshold (spatial correlation), and these clusters remain unchanged as long as the sensor values stay within a threshold over time (temporal correlation). With CAG, only one sensor reading per cluster is transmitted whereas with Tiny AGgregation (TAG) all the nodes in the network transmit the sensor readings. Thus, CAG provides energy efficient and approximate aggregation results with small and often negligible and bounded error.
 In this article we extend our initial work in CAG in five directions: First, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Second, we design CAG for two modes of operation (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode provides mechanisms for one-shot queries, whereas the streaming mode provides those for continuous queries. Third, we propose a fixed range clustering method, which makes the performance of our system independent of the magnitude of the sensor readings and the network topology. Fourth, using mica2 motes, we perform a large-scale measurement of real environmental data (temperature and light, both indoor and outdoor) and the wireless radio reliability, which were used for both analytical modeling and simulation experiments. Fifth, we model the spatially correlated data using the properties of our real world measurements.
 Our experimental results show that when we compute the average of sensor readings in the network using the CAG interactive mode with the user-provided error threshold of, 20%, we can save 68.25% of transmissions over TAG with only 2.46% inaccuracy in the result. The streaming mode of CAG can save even more transmissions (up to 70.24% in our experiments) over TAG, when data shows high spatial and temporal correlations. We expect these results to hold in reality, because we used the mica2 radio profile and empirical datasets for our simulation study. CAG is the first system that leverages spatial and temporal correlations to improve energy efficiency of in-network aggregation. This study analytically and empirically validates CAG's effectiveness.
12220012
Sweep-based human deformation
We present a sweep-based approach to human body modeling and deformation. A rigid 3D human model, given as a polygonal mesh, is approximated with control sweep surfaces. The vertices on the mesh are bound to nearby sweep surfaces and then follow the deformation of the sweep surfaces as the model bends and twists its arms, legs, spine and neck. Anatomical features including bone-protrusion, muscle-bulge, and skin-folding are supported by a GPU-based collision detection procedure. The volumes of arms, legs, and torso are kept constant by a simple control using a volume integral formula for sweep surfaces. We demonstrate the effectiveness of this sweep-based human deformation in several test animation clips.
477360
Approximate Policy Iteration with a Policy Language Bias
We explore approximate policy iteration, replacing the usual cost-function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.