2912663
Confidence Based Dual Reinforcement Q-Routing: An adaptive online network routing algorithm
This paper describes and evaluates the Confidence-based Dual Reinforcement Q-Routing algorithm (CDRQ-Routing) for adaptive packet routing in communication networks. CDRQ-Routing is based on the Q-learning framework of Q-Routing. The main contribution of this work is the increased quantity and improved quality of exploration in CDRQ-Routing, which lead to faster adaptation and better routing policies learned as compared to Q-Routing, the state-of-the-art adaptive Bellman-Ford Routing, and the non-adaptive shortest path routing. Experiments over several network topologies have shown that at different loads, CDRQ-Routing learns superior policies significantly faster than Q-Routing. Moreover, CDRQ-Routing learns policies that sustain higher load levels than Q-Routing. Analysis shows that overhead due to exploration is insignificant as eqmpared to the improvements in CDRQ-Routing.
14954871
Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
Abstract: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.
120407
A Hierarchical Multiclassifier System for Hyperspectral Data Analysis
Many real world classification problems involve high dimensional inputs and a large number of classes. Feature extraction and modular learning approaches can be used to simplify such problems. In this paper, we introduce a hierarchical multiclassifier paradigm in which a C- class problem is recursively decomposed into C - 1 two-class problems. A generalized modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problem of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier architecture was used to classify 12 types of landcover from 183-dimensional hyperspectral data. The classification accuracy was significantly improved by 4 to 10% relative to other feature extraction and modular learning approaches. Moreover, the class hierarchy that was automatically discovered conformed very well with a human domain expert's opinion, which demonstrates the potential of such a modular learning approach for discovering domain knowledge automatically from data.
207251944
Discriminative Random Fields
In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy. In this work, we present Discriminative Random Fields (DRFs) to model spatial interactions in images in a discriminative framework based on the concept of Conditional Random Fields proposed by lafferty et al.(2001). The DRFs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data. The DRF framework offers several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in computer vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the MRF framework. Third, the interaction in labels in DRFs is based on the idea of pairwise discrimination of the observed data making it data-adaptive instead of being fixed a priori as in MRFs. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where the likelihood parameters are usually learned separately from the field parameters. We present preliminary experiments with man-made structure detection and binary image restoration tasks, and compare the DRF results with the MRF results.
13984585
A novel progressive thick slab paradigm for volumetric medical image compression and navigation
In this paper, we propose a novel thick slab paradigm which provides an efficient scheme to navigate through large three dimensional (3-D) medical data sets within the framework of wavelet based lossless volumetric compression. We also propose a 3-D wavelet-like multi-resolution decomposition to perform 3-D image compression, which provides better compression ratio and 3 times faster decoding than JPEG-2000 for large data sets of computed tomography modality, along with a clinical workflow for navigation application. The proposed method suits radiology applications better than JPEG-2000 as it improves the productivity of radiologists by reducing their waiting time while the data is being transferred from a remote database over the network, along with reduction in diagnosis time, by providing an efficient navigation scheme with high compression performance.
35719472
Hierarchical Back-Face Computation
We present a sub-linear algorithm to compute the set of back-facing polygons in a polyhedral model. The algorithm partitions the model into hierarchical clusters based on the orientations and positions of the polygons. As a pre-processing step, the algorithm constructs spatial decompositions with respect to each cluster. For a sequence of back-face computations, the algorithm exploits the coherence in view-point movement to efficiently determine if it is in front of or behind a cluster. Due to coherence, the algorithm’s performance is linear in the number of clusters on average. We have applied this algorithm to speed up the rendering of polyhedral models. On average, we are able to cull almost half the polygons. The algorithm accounts for 5 – 10% of the total CPU time per frame on an SGI Indigo2 Extreme. The overall frame rate is improved by 40 – 75% as compared to the standard back-face culling implemented in hardware.
14746742
Digital tapestry [automatic image synthesis]
This paper addresses the novel problem of automatically synthesizing an output image from a large collection of different input images. The synthesized image, called a digital tapestry, can be viewed as a visual summary or a virtual 'thumbnail' of all the images in the input collection. The problem of creating the tapestry is cast as a multi-class labeling problem such that each region in the tapestry is constructed from input image blocks that are salient and such that neighboring blocks satisfy spatial compatibility. This is formulated using a Markov random field and optimized via the graph cut based expansion move algorithm. The standard expansion move algorithm can only handle energies with metric terms, while our energy contains non-metric (soft and hard) constraints. Therefore we propose two novel contributions. First, we extend the expansion move algorithm for energy functions with non-metric hard constraints. Secondly, we modify it for functions with "almost" metric soft terms, and show that it gives good results in practice. The proposed framework was tested on several consumer photograph collections, and the results are presented.
1474217
An observation-constrained generative approach for probabilistic classification of image regions
Abstract In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A Kullback–Leibler Divergence-based fast model selection procedure is also proposed for learning mixture models in a low dimensional feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.