International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Accurate Color Discrimination with Classification Based on Feature Distributions
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
A new algorithm to compute the distance between multi-dimensional histograms
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Computers in Biology and Medicine
Imaging technologies applied to chronic wounds: a survey
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
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The work investigates the use of multi dimensional histograms for segmentation of images of chronic wounds. We employ a Support Vector Machine (SVM) classifier for automatic extraction of wound region from an image. We show that the SVM classifier can generalize well on the difficult wound segmentation problem using only 3-D dimensional color histograms. We also show that color histograms of higher dimensions provide a better cue for robust separation of classes in the feature space. A key condition for the successful segmentation is an efficient sampling of multi-dimensional histograms. We propose a multi-dimensional histogram sampling technique for generation of input feature vectors for the SVM classifier. We compare the performance of the multi-dimensional histogram sampling with several existing techniques for quantization of 3-D color space. Our experimental results indicate that different sampling techniques used for the generation of input feature vectors may increase the performance of wound segmentation by about 25%.