JOURNAL ARTICLE

Saliency detection via textural contrast

Wonjun KimChangick Kim

Year: 2012 Journal:   Optics Letters Vol: 37 (9)Pages: 1550-1550   Publisher: Optica Publishing Group

Abstract

We present a new approach for visual saliency detection from various natural images. It is inspired by our careful observation that the human visual system (HVS) responds sensitively and quickly to high textural contrast, derived from the discriminative directional pattern from its surroundings as well as the noticeable luminance difference, for understanding a given scene. By formulating such textural contrast within a multiscale framework, we construct a more reliable saliency map even without color information when compared to most previous approaches still suffering from the complex and cluttered background. The proposed method has been extensively tested on a wide range of natural images, and experimental results show that the proposed scheme is effective in detecting visual saliency compared to various state-of-the-art methods.

Keywords:
Artificial intelligence Contrast (vision) Computer science Luminance Discriminative model Computer vision Pattern recognition (psychology) Human visual system model Construct (python library) Saliency map Optics Image (mathematics) Physics

Metrics

12
Cited By
2.21
FWCI (Field Weighted Citation Impact)
5
Refs
0.89
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Olfactory and Sensory Function Studies
Life Sciences →  Neuroscience →  Sensory Systems
Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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