Bottom-up visual attention model for still image: a preliminary study

International Journal of Advances in Intelligent Informatics

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Title Bottom-up visual attention model for still image: a preliminary study
Creator Prahara, Adhi
Murinto, Murinto
Ismi, Dewi Pramudi
Subject Visual attention; Bottom-up attention; Saliency map; Computer vision; Curiosity
Description The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.
Publisher Universitas Ahmad Dahlan
Date 2020-03-31
Type info:eu-repo/semantics/article

Format application/pdf
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 1 (2020): March 2020; 82-96
Language eng

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