Does Color Influence Image Complexity Perception?

Ciocca, Gianluigi; Corchs, Silvia; Gasparini, Francesca; Bricolo, Emanuela; Tebano, R.

doi:10.1007/978-3-319-15979-9_14

Cited by 19 publications

(14 citation statements)

References 14 publications

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“…The orthographic image content features are based on the intensities of pixels. Lightness information influences the perception of real-world images with a higher degree than color [15,32]. In the simplest case, a group of pixels with similar intensity values being close to each other constitute a region.…”

Section: Subjective Visual Features Of the Satellite Image Contentmentioning

confidence: 99%

“…We take into account all types of textures because we analyze visual features throughout the image perceivable by humans. We have selected the most characteristic visual features of orthographic images, such as the size of the individual regions, contrast, roughness, density, and the number of different regions [1,[11][12][13][14][15][16].…”

Section: Subjective Visual Features Of the Satellite Image Contentmentioning

confidence: 99%

“…The color feature can be included for respondents besides the intensity to separate regions. There is a correlation between color and intensity, evaluating the complexity of the image, but the color information does not influence the perception significantly [15].…”

Section: Subjective Visual Features Of the Satellite Image Contentmentioning

confidence: 99%

“…Most of the authors [1,[11][12][13] have studied the visual characteristics of the individual types of textures in the isolated areas of an image. Our selection of subjective visual features of the satellite images is based on the scientists' works [1,[11][12][13][14][15][16], and validated with the set of experiments. This set of visual characteristics has been applied for the edge detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

Baušys

Kazakeviciute-Januskeviciene

Cavallaro

et al. 2020

Sustainability

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Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.

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Section: Subjective Visual Features Of the Satellite Image Contentmentioning

confidence: 99%

Section: Subjective Visual Features Of the Satellite Image Contentmentioning

confidence: 99%

Section: Subjective Visual Features Of the Satellite Image Contentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

Baušys

Kazakeviciute-Januskeviciene

Cavallaro

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Corchs et al [16] proposed a new complexity measure where different image features, based on spatial, frequency and color properties were linearly combined. Zhou et al [17] evaluated image complexity by using color feature as well as other spatial features. Ciocca et al [18] investigated the relationship between image complexity and ten features, such as the gray-level entropy and the target co-occurrence matrix.…”

Section: Introductionmentioning

confidence: 99%

Complexity estimation of image sequence for automatic target track

Wang

Zhang

Yan

2019

Journal of Systems Engineering and Electronics

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In the field of automatic target recognition and tracking, traditional image complexity metrics, such as statistical variance and signal-to-noise ratio, all focus on single-frame images. However, there are few researches about the complexity of image sequence. To solve this problem, a criterion of evaluating image sequence complexity is proposed. Firstly, to characterize this criterion quantitatively, two metrics for measuring the complexity of image sequence, namely feature space similarity degree of global background (FSSDGB) and feature space occultation degree of local background (FSODLB) are developed. Here, FSSDGB reflects the ability of global background to introduce false alarms based on feature space, and FSODLB represents the difference between target and local background based on feature space. Secondly, the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images. Finally, the image sequence complexity is not a linear sum of the single-frame image complexity. Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well. The nonlinear transformation based on median (NTM) is proposed to construct complexity of image sequence. The experimental results show that the proposed metric is more valid than other metrics, such as sequence correlation (SC) and interframe change degree (IFCD), and it is highly relevant to the actual performance of automatic target tracking algorithms.

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