Improving unsupervised saliency detection by migrating from RGB to multispectral images

Abstract

Saliency detection has been an important topic during the last decade. The main goal of saliency detection models is to detect the most relevant objects in a given scene. Most of these models use RGB (Red, Green, Blue) images as an input because they mainly focus on applications where features (eg, faces, textures, colors, or human silhouettes) are extracted from color images, and there are many labeled databases available for RGB-based saliency data. Nevertheless, the use of RGB inputs clearly limits the amount of information from where to extract the salient regions as spectral information is lost during the color image recording. On the contrary, multispectral systems are able to capture more than three bands in a single capture and can retrieve information from the full spectrum at a pixel. The main aim of this study is to investigate the advantages of using multispectral images instead of RGB images for saliency detection within the framework of unsupervised models. We compare the performance of several unsupervised saliency models with both RGB and multispectral images using a specific dataset of multispectral images with ground-truth data extracted from observers' fixation patterns. Our results show a general improvement when multispectral information is taken into account. The saliency maps estimated by using the multispectral features are closer to the ground-truth data, with the simplest Graph-based visual saliency and Boolean Map-based models showing good relative gain compared with other approaches.