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.
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.
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.
8040923
Inductive Policy Selection for First-Order MDPs
We select policies for large Markov Decision Processes (MDPs) with compact first-order representations. We find policies that generalize well as the number of objects in the domain grows, potentially without bound. Existing dynamic-programming approaches based on flat, propositional, or first-order representations either are impractical here or do not naturally scale as the number of objects grows without bound. We implement and evaluate an alternative approach that induces first-order policies using training data constructed by solving small problem instances using PGraphplan (Blurn & Langford, 1999). Our policies are represented as ensembles of decision lists, using a taxonomic concept language. This approach extends the work of Martin and Geffner (2000) to stochastic domains, ensemble learning, and a wider variety of problems. Empirically, we find "good" policies for several stochastic first-order MDPs that are beyond the scope of previous approaches. We also discuss the application of this work to the relational reinforcement-learning problem.
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.
53234423
Cache-oblivious mesh layouts
We present a novel method for computing cache-oblivious layouts of large meshes that improve the performance of interactive visualization and geometric processing algorithms. Given that the mesh is accessed in a reasonably coherent manner, we assume no particular data access patterns or cache parameters of the memory hierarchy involved in the computation. Furthermore, our formulation extends directly to computing layouts of multi-resolution and bounding volume hierarchies of large meshes.We develop a simple and practical cache-oblivious metric for estimating cache misses. Computing a coherent mesh layout is reduced to a combinatorial optimization problem. We designed and implemented an out-of-core multilevel minimization algorithm and tested its performance on unstructured meshes composed of tens to hundreds of millions of triangles. Our layouts can significantly reduce the number of cache misses. We have observed 2--20 times speedups in view-dependent rendering, collision detection, and isocontour extraction without any modification of the algorithms or runtime applications.
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.
7105370
Realistic human hand deformation
We present a new approach to realistic hand modeling and deformation with real-time performance. We model the underlying shape of a human hand by means of sweeps which follow a simplified skeleton. The resulting swept surfaces are blended, and an auxiliary surface is then bound to the swept representation in the palm region. In the areas of this palm-control surface where bulges occur in certain poses of a real hand, the vertices are given their own trajectories, so that the palm forms realistic shapes as the joints bend. Palm lines can also be modeled as valleys in the skin by sketching them on a displacement map on the palm-control surface, and activitating them when appropriate joint movements take place. Self-intersections and collisions are detected using geometric primitives that are automatically generated from, and deform with, the sweeps and palm surface. Our algorithm runs in real time, and the naturalism of its results are demonstrated by comparative images of modeled and real hands, including several challenging poses. Copyright © 2006 John Wiley & Sons, Ltd.