Abstract
In recent years, binary coding of image features, such as local binary patterns and local phase quantization, have become popular in a large variety of image quantification tasks. Lately, some non-binary codings, such as local ternary pattern, have been proposed to improve the performance of these binary based approaches. In these methods it is very important to correctly choose the thresholds applied for building the coding used to represent a given image and its features by a feature vector. Keywords: Machine learning, non-binary coding, stem cell image, sub-cellular image, support vector machine, texture descriptors, Local Binary Pattern, Local Phase Quantization, Neighborhood Topology, Lysosome, Microtubules, Mitochondria, Mitochondria, Endoplasmic Reticulum, Plasma Membrane, Actin filaments
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