Section Article

Enhancing the HG Method for Retrieving Landscape Images Based on Content
Author(s): Shahrukh Sheikh

Abstract
Landscape image retrieval has become increasingly important in the fields of computer vision environmental monitoring geospatial intelligence visual search engines natural scene analysis tourism planning and digital archiving. Traditional content-based image retrieval (CBIR) techniques rely heavily on low-level features such as colour histograms texture filters and simple spatial descriptors which fail to accurately represent the structural and semantic richness of natural landscapes. The HG Method—an earlier hybrid-gradient technique—emerged as a significant advancement by integrating gradient-based directional patterns with traditional colour-texture descriptors. Yet up to 2018 the HG Method still suffered from several limitations including sensitivity to illumination variance noise-prone gradient vectors difficulty distinguishing complex natural textures limited scalability weak semantic generalisation and inconsistent retrieval accuracy across diverse landscape categories.