Enhancing Image Classification through Exploitation of Hue Cyclicity in Convolutional Neural Networks

Abstract

This study introduces innovative methodologies for image classification employing Convolutional Neural Networks (CNNs) by leveraging the cyclical attributes of hue within the HSV color space. Two distinct kernels are explored to linearize the circular values of hue. The first kernel converts the angular values to three modulo distance values corresponding to three color hue points. The second kernel utilizes trigonometry to convert angles into sine and cosine linear values. Experimental evaluations demonstrate that linearizing hue values leads to a notable enhancement in classification accuracy. This research provides insights into optimizing CNN-based image classification by integrating hue cyclicity, thereby advancing the capabilities of computer vision systems.