The aim of image mining is to extract valuable knowledge from image data. In the context of supervised image classification, we want to assign automatically a label to image from their visual content. The whole process is identical to the standard data mining process. We learn a classifier from a set of classified images. Then, we can apply the classifier to a new image in order to predict its class membership. The particularity is that we must extract a vector of numerical features from the image before to launch the machine learning algorithm, and before to apply the classifier in the deployment phase.
We deal with an image classification task in this tutorial. The goal is to detect automatically the images which contain a car. The main result is that, even if I have a basic knowledge about the image processing, I can lead the analysis with a facility which is symptomatic of the usability of Knime in this context.
Keywords: image mining, image classification, image processing, feature extraction, decision tree, random forest, knime
Tutorial: en_Tanagra_Image_Mining_Knime.pdf
Dataset and program (Knime archive): image mining tutorial
References:
Knime Image Processing, https://tech.knime.org/community/image-processing
S. Agarwal, A. Awan, D. Roth, « UIUC Image Database for Car Detection » ; https://cogcomp.cs.illinois.edu/Data/Car/
