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.
In this work we compare several approaches for extracting local ternary/quinary pattern image features and ternary coding
for local phase quantization on various types of biological microscope images using six image databases for sub-cellular
and stem cell image classification. We use these image features for training a stand-alone support vector machine and a
random subspace of support vector machines to separate the different classes present in each dataset. Moreover, several
distance measures are tested.
Our results show that, on the chosen datasets, the best approach uses a multi-threshold local quinary coding. The use of a
more discriminating coding scheme than the binary one, combined with a pool of thresholds, helps in distinguishing
descriptive features from noise, thus improving classification results. The Matlab code is available at
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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