Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features

Schnitzspan, Paul; Fritz, Mario; Schiele, Bernt

doi:10.1007/978-3-540-88688-4_39

Cited by 25 publications

(22 citation statements)

References 14 publications

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“…SVRF (Schnitzspan et al 2008;Lee et al 2005) is a Discrete Random Field (DRF) based extension for SVM. It considers interactions in the labels of adjacent data points while preserving the same appealing generalization properties as the Support Vector Machine (SVM).…”

Section: N-dimensional Classifiersmentioning

confidence: 99%

Spatial Analysis in Public Health Domain: An NLP Approach

Arun

2013

Geodesy and Cartography

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Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.

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Section: N-dimensional Classifiersmentioning

confidence: 99%

Spatial Analysis in Public Health Domain: An NLP Approach

Arun

2013

Geodesy and Cartography

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“…Schnitzspan et al (2008) propose hierarchical support vector random fields that SVM is used as a classifier for unary potentials in conditional random field framework. While the training and cross-validation steps in SVM are time consuming, randomized decision forest (RDF) (Breiman, 2001) is introduced to significantly speed up the learning and prediction process.…”

Section: Related Workmentioning

confidence: 99%

Feature Evaluation for Building Facade Images – An Empirical Study

Min-hua

Förstner

Chai

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

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“…Spectral and spatial information have been combined to increase the object class seperability to yield higher registration accuracy (Mercier, Lennon 2003). An adaptive kernel strategy (Srivastava 2004) along with Support Vector Random Field (SVRF) (Schnitzspan et al 2008;Lee et al 2005; Hosseini, Homayouni 2009) has been adopted for achieving a semi-supervised classification approach. Main obstruct in the modelling of features using Cellular Neural Network (CNN) (Mitchell et al 1996) approach was increased computational complexity, which has been effectively tackled using coreset (Agarwal et al 2001) based approximation.…”

Section: Introductionmentioning

confidence: 99%

A CNN Based Hybrid Approach Towards Automatic Image Registration

Arun

2013

Geodesy and Cartography

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Abstract. Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network has been found to be effective in improving feature matching as well as resampling stages of registration, and complexity of the approach has been considerably reduced using corset optimization. CNN-prolog based approach has been adopted to dynamically use spectral and spatial information for representing contextual knowledge. The salient features of this work are feature point optimisation, adaptive resampling and intelligent object modelling. Investigations over various satellite images revealed that considerable success has been achieved with the procedure. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

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Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features

Cited by 25 publications

References 14 publications

Spatial Analysis in Public Health Domain: An NLP Approach

Spatial Analysis in Public Health Domain: An NLP Approach

Feature Evaluation for Building Facade Images – An Empirical Study

A CNN Based Hybrid Approach Towards Automatic Image Registration

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