Building look &amp; feel concept models from color combinations

Skaff, Sandra; Marchesotti, Luca; Saunders, Craig

doi:10.1007/s00371-011-0657-9

Cited by 16 publications

(15 citation statements)

References 11 publications

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“…Affective concepts were modeled using color palettes, which showed that the bag of colors and Fisher vectors (i.e., higher order statistics about the distribution of local descriptors) were effective [9]. Zhang et al [27] characterized shape through Zernike features, edge statistics features, object statistics, and Gabor filters.…”

Section: Related Workmentioning

confidence: 99%

“…We represented emotion as a tuple consisting of valence and arousal values. The values of valence and arousal were in the range of (1,9). In order to predict the values of valence and arousal we proposed to learn a regression model for either dimension separately.…”

Section: Fitting the Dimensionality Of Emotionmentioning

confidence: 99%

“…However, the computational work on the IAPS dataset to understand the visual factors that affect emotions has been preliminary. Researchers [9,11,18,23,25,26] investigated factors such as color, texture, composition, and simple semantics to understand emotions, but have not quantitatively addressed the effect of perceptual shapes. The study that did explore shapes by Zhang et al [27] predicted emotions evoked by viewing abstract art images through low-level features like color, shape, and texture.…”

Section: Introductionmentioning

confidence: 99%

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On shape and the computability of emotions

Suryanarayan

Adams

et al. 2012

Proceedings of the 20th ACM International Conference on Multimedia

138

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We investigated how shape features in natural images influence emotions aroused in human beings. Shapes and their characteristics such as roundness, angularity, simplicity, and complexity have been postulated to affect the emotional responses of human beings in the field of visual arts and psychology.However, no prior research has modeled the dimensionality of emotions aroused by roundness and angularity.Our contributions include an in-depth statistical analysis to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset we provide evidence for the significance of roundness-angularity and simplicitycomplexity on predicting emotional content in images. We combine our shape features with other state-of-theart features to show a gain in prediction and classification accuracy.We model emotions from a dimensional perspective in order to predict valence and arousal ratings which have advantages over modeling the traditional discrete emotional categories. Finally, we distinguish images with strong emotional content from emotionally neutral images with high accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Fitting the Dimensionality Of Emotionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On shape and the computability of emotions

Suryanarayan

Adams

et al. 2012

Proceedings of the 20th ACM International Conference on Multimedia

138

View full text Add to dashboard Cite

show abstract

“…Csurka et al [CSMS11] associated 15 affective words with groups of colors, then performed color transfer referencing the established color groups. Csurka et al [CSMS11] associated 15 affective words with groups of colors, then performed color transfer referencing the established color groups.…”

Section: Introductionmentioning

confidence: 99%

Image Recoloring with Valence‐Arousal Emotion Model

Kim

Kang

Lee

2016

Computer Graphics Forum

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SourceResult + Negative (1.0/N) Target + Positive (9.0/N) Source Result Target Figure 1: Given a source image and target emotion (towards more positive or negative), the system recolors the image using reference image segments selected to match the target emotion as well as the labels of the semantically segmented regions. AbstractWe introduce an affective image recoloring method for changing the overall mood in the image in a numerically measurable way. Given a semantically segmented source image and a target emotion, our system finds reference image segments from the collection of images that have been tagged via crowdsourcing with numerically measured emotion labels. We then recolorize the source segments using colors from the selected target segments while preserving the gradient of the source image to generate a seamless and natural result. User study confirms the effectiveness of our method in accomplishing the stated goal of altering the mood of the image to match the target emotion level.

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“…An important aspect providing much of the visual perception of an image is the composition of colors. Csurka et al [1] abstracted the concepts of look and feel (e.g. capricious, classic, cool, and delicate impressions) from an image according to color combinations.…”

Section: Introductionmentioning

confidence: 99%

Video color conceptualization using optimization

Cao

Zhang

Guo

et al. 2014

Sci. China Inf. Sci.

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Color conceptualization aims to propagate "color concepts" from a library of natural color images to the input image by changing the main color. However, the existing method may lead to spatial discontinuities in images because of the absence of a spatial consistency constraint. In this paper, to solve this problem, we present a novel method to force neighboring pixels with similar intensities to have similar color. Using this constraint, the color conceptualization is formalized as an optimization problem with a quadratic cost function. Moreover, we further expand two-dimensional (still image) color conceptualization to three-dimensional (video), and use the information of neighboring pixels in both space and time to improve the consistency between neighboring frames. The performance of our proposed method is demonstrated for a variety of images and video sequences.

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Building look & feel concept models from color combinations

Cited by 16 publications

References 11 publications

On shape and the computability of emotions

On shape and the computability of emotions

Image Recoloring with Valence‐Arousal Emotion Model

Video color conceptualization using optimization

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