A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

Abstract

In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.

Color education: A study on methods of influence on memory

“…This result enables us to state that color draws attention during the academic process better than other visual characteristics do. This confirms the research results by Mogas-Recalde and Palau (2021) , Liu and Wang (2021) and Bruno et al. (2020) .…”

“… Morita & Kambara (2021) investigated that typical colors had better help memorize than blurred. Specialists investigated the presence of relationships between visibility and perceptually homogeneous color space, which can also significantly affect the processes of concentration and memorization ( Bruno et al., 2020 ).…”

SATSal: A Multi-Level Self-Attention Based Architecture for Visual Saliency Prediction

“…Achanta et al [2] extracted salient pixels in images using features of colour and luminance in the Fourier domain. Colour spaces and their role in saliency extraction were further investigated later by several researchers, e.g., [13].…”

Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling

“…Aggregation-based Models. Most modern saliency detection models are based on the encoder-decoder framework to integrate multi-level features and leverage contextual information across different layers [3,4,12,15,22,25,27,30,33,36,38,39,[43][44][45][46]. The encoder is often used to extract multi-level features from the image, and the decoder is designed to effectively combine the features and predict the saliency map.…”