Effective image data points extraction via decision tree based on feature classifier

Abstract

Image data points mining is concerned with the extraction of knowledge relationship among image data and other patterns inherent in the images. Taxonomy-Aware Catalog Integration (TCI) processing step ensures that the master taxonomy Rule-based Multivariate Text Feature Selection (RMTFS) method takes into consideration both the semantic information and the syntactic relationships between n-gram features. In realizing the relationship between the attributes, the Decision Tree-based Label Feature Classifier (DTLFC) mechanism is proposed. In the initial step in the DTLFC mechanism, pixel-wise image feature points are extracted and the same is converted into a table of a database. The feature descriptor thus formed by combining a features set and a specific pixel's label is represented by a tuple of the transformed database table and helps in the generation of the decision tree using a given image points data set in constructing a pixel-wise image processing model. Both experimental and theoretical analysis demonstrate that the DTLFC mechanism can attain a very efficient and effective level of classification on image data points. Compared with the TCI and RMTFS methods, the DTLFC mechanism creates an effective feature classifier in terms of filtering efficiency, classification accuracy, processing time, computational cost, scalability, and the intensity rate of dominant attributes.