scholarly journals Low-level visual saliency does not predict change detection in natural scenes

2007 ◽  
Vol 7 (10) ◽  
pp. 3 ◽  
Author(s):  
Jonathan A. Stirk ◽  
Geoffrey Underwood
Keyword(s):  
Change Detection ◽  
Visual Saliency ◽  
Natural Scenes ◽  
Low Level

scholarly journals SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

2018 ◽  
Vol XLII-3 ◽  
pp. 341-348
Author(s):  
W. Feng ◽  
H. Sui ◽  
X. Chen
Keyword(s):  
Random Forest ◽  
Change Detection ◽  
Learning Algorithm ◽  
Saliency Detection ◽  
Principal Component ◽  
Visual Saliency ◽  
Saliency Map ◽  
Difference Image ◽  
Fcm Clustering ◽  
Optimal Segmentation

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy <i>c</i>-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

A Visual Saliency Detection Approach by Fusing Low-Level Priors With High-Level Priors

2019 ◽  
Vol 9 (3) ◽  
pp. 23-37
Author(s):  
Monika Singh ◽  
Anand Singh Singh Jalal ◽  
Ruchira Manke ◽  
Aamir Khan
Keyword(s):  
Minimum Distance ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Research Area ◽  
Experimental Results ◽  
Low Level ◽  
Detection Approach ◽  
Art Methods ◽  
High Level ◽  
Visual Saliency Detection

Saliency detection has always been a challenging and interesting research area for researchers. The existing methodologies either focus on foreground regions or background regions of an image by computing low-level features. However, considering only low-level features did not produce worthy results. In this paper, low-level features, which are extracted using super pixels, are embodied with high-level priors. The background features are assumed as the low-level prior due to the similarity in the background areas and boundary of an image which are interconnected and have minimum distance in between them. High-level priors such as location, color, and semantic prior are incorporated with low-level prior to spotlight the salient area in the image. The experimental results illustrate that the proposed approach outperform the sate-of-the-art methods.

scholarly journals Low-level image statistics in natural scenes influence perceptual decision-making

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Noor Seijdel ◽  
Sara Jahfari ◽  
Iris I. A. Groen ◽  
H. Steven Scholte
Keyword(s):  
Decision Making ◽  
Natural Scenes ◽  
Perceptual Decision Making ◽  
Image Statistics ◽  
Perceptual Decision ◽  
Low Level

Low-level and high-level prior learning for visual saliency estimation

2014 ◽  
Vol 281 ◽  
pp. 573-585 ◽  
Author(s):  
Mingli Song ◽  
Chun Chen ◽  
Senlin Wang ◽  
Yezhou Yang
Keyword(s):  
Visual Saliency ◽  
Prior Learning ◽  
Low Level ◽  
High Level

scholarly journals Natural scenes statistics and visual saliency

2010 ◽  
Vol 10 (7) ◽  
pp. 1358-1358
Author(s):  
J. Xu ◽  
J. Tsien ◽  
Z. Yang
Keyword(s):  
Visual Saliency ◽  
Natural Scenes

scholarly journals Change detection with cross enhancement of high‐ and low‐level change‐related features

2021 ◽  
Author(s):  
Rui Huang ◽  
Yan Xing ◽  
Mo Zhou ◽  
Ruofei Wang
Keyword(s):  
Change Detection ◽  
Low Level ◽  
Level Change

scholarly journals Gaze Patterns and Visual Salience in Change Detection of Natural Scenes

2010 ◽  
Vol 10 (7) ◽  
pp. 169-169 ◽  
Author(s):  
T. W. Boyer ◽  
C. Yu ◽  
T. Smith ◽  
B. I. Bertenthal
Keyword(s):  
Change Detection ◽  
Natural Scenes ◽  
Visual Salience ◽  
Gaze Patterns

scholarly journals Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

2020 ◽  
Vol 13 ◽  
pp. 6260-6276
Author(s):  
Xin Wang ◽  
Peijun Du ◽  
Dongmei Chen ◽  
Sicong Liu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Change Detection ◽  
Low Level ◽  
High Level
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