Multi Channel Classification and Clustering System
This project is maintained by OpenImageAnalysisGroup
Here we present an image-based Multi Channel Classification and Clustering System (MCCCS). It is a generalized, script-based classification system for processing various kinds of image data. Due to the modular design, individual processing-components can be easily adapted, extended or exchanged by other external commands. The system includes pipeline examples for solving different segmentation, classification and clustering problems. For solving these various tasks we are utilizing common machine learning approaches. The conversion of image pixel data to the common ARFF file format encouraged the usage of a wide variety of classification frameworks.
MCCCS is a system utilizing machine learning techniques for image processing and image analysis.
The system is generalized to handle a diverse set of input data, RGB images and multi-channel (hyper-spectral) datasets as well.
The system includes different approaches for image feature extraction (color and texture).
It is able to solve different classification problems by using supervised and un-supervised machine learning methods provided by exchangeable libraries.
It includes methods for handling multi-channel data to solve multi-label classification problems in an efficient way.
Due to its modular Bash-script-based design, it is also easily adaptable and extensible by using common image processing, machine learning libraries or own algorithms.
The software is implemented as a set of Bash scripts which have been tested under Linux, Mac and Windows.
The provided commands are mainly implemented using the JAVA (Version 1.8) programming language, due to the advantage of its platform independence and broad support of different libraries and toolboxes like WEKA and ImageJ.
This work was supported by IPK institute funds and project funding of the Federal Ministry of Education and Research (BMBF) (DPPN: 031A053B).