Probabilistic Classifier for Fashion Image Grouping Using Multi-Layer Feature Extraction Model

Abstract

In this article, probabilistic classification model is designed for the fashion-related images collected from social networks. The proposed model is divided into two parts. The first is feature extraction where six important features are taken into consideration to deal with heterogeneous nature of the given images. The second classification is done with the help of probability computations to get collection of homogeneous images. Here, class-conditional probability of extracted features are calculated, then joint probability is used for the classification. Class label with maximum joint probability is assigned to the given image. A comparative study of proposed classification model with existing popular supervised as well as unsupervised classification approaches is done on the basis of obtained accuracy of the results. The effect of convolutional neural network inclusion in the proposed feature extraction model is also shown where it improves the accuracy of final results. The output of this system is useful further for fashion trend analysis.