Cosine modulated filter bank‐based architecture for extracting and fusing saliency features

Abstract

AbstractMany academics are interested in content‐based image retrieval techniques like image segmentation. In computer vision, the most popular method for segmenting a digital image into different parts is known as image segmentation. We assigned the artificially intelligent algorithm to the image's critical areas by modeling human features in specific regions. In order to detect the object and identify the key parts in the ‘RGB’ photographs, we combined scenes based on a colour and depth map, or ‘RGB‐D’, and used cosine modulated filter bank (CMFB), which conducts cross‐scale extraction of joint features from the images during feature extraction. The proposed ‘CMFB’ combines the discovered collaborative elements with the discovered supplementary data. The features in multi‐scale images is combined using fusion blocks with the goal of producing additional features (FB). Then, a saliency mapping calculation is made for the loss linked to two blocks. The suggested ‘CMFB’ is tested with the aid of five data sets, and it is shown that, the proposed ‘CMFB’ outperforms other conventional techniques.