Pixels to Precision: Features Fusion and Random Forests over Labelled-based Segmentation

Object classification is a crucial yet challenging vision ability to perfect The fundamental objective is to educate computers to understand visuals the same way humans do. Due to variables like brightness, distance, and backdrop that affect object appearance and recognition, disagreements about accurate object categorization still exist despite tremendous advances. In this paper, the initial preprocessing attempts to carry out comes before any later analyses. Following this preprocessing stage, the image will be divided into several sections based on hue similarity. Following methodical alteration, these segmented portrayals will be reduced to their essential components in order to make it easier to recognize the target object. The Random Forest algorithm's use for classification purposes is described in depth, leading to an overall accuracy rate of 89.70%.