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Deep Cognitive Gate : Resembling Human Cognition for Saliency Detection

Saliency detection by human refers to the ability to identify pertinent information using our perceptive and cognitive capabilities. While human perception is attracted by visual stimuli, our cognitive capability is derived from the inspiration of constructing concepts of reasoning. Saliency detection has gained intensive interest with the aim of resembling human ‘perceptual’ system. However, saliency related to human ‘cognition’, particularly the analysis of complex salient regions (‘cogitating’ process), is yet to be fully exploited. We propose to resemble human cognition, coupled with human perception, to improve saliency detection. We recognize saliency in three phases (‘Seeing’ - ‘Perceiving’ - ‘Cogitating), mimicking human's perceptive and cognitive thinking of an image. In our method, ‘Seeing’ phase is related to human perception, and we formulate the ‘Perceiving’ and ‘Cogitating’ phases related to the human cognition systems via deep neural networks (DNNs) to construct a new module (Cognitive Gate) that enhances the DNN features for saliency detection. To the best of our knowledge, this is the first work that established DNNs to resemble human cognition for saliency detection. In our experiments, our approach outperformed 17 benchmarking DNN methods on six well-recognized datasets, demonstrating that resembling human cognition improves saliency detection.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.9 Part 1 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4776-4792

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

IMAGE PROCESSING
COGNITION
NEURAL NETWORKS
OBJECT DETECTION

Info Detail Spesifik

10.1109/TPAMI.2021.3068277

Pernyataan Tanggungjawab

Ke Yan, Xiuying Wang, Jinman Kim, Wangmeng Zuo, Dagan Feng

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