Validation of an Aesthetic Assessment System for Commercial Tasks

Rodríguez-Fernández, Nereida; Alvarez-Gonzalez, Sara; Santos, Iria; Torrente-Patiño, Alvaro; Carballal, Adrián; Romero, Juan

doi:10.3390/e24010103

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…In addition, they spend a great deal of time creating these ads, especially in the visual part. PhotoILike [33] developed a system that scores and sorts real estate images according to their commercial impact in order to achieve a higher conversion rate. At this point, experiments with IE were considered to increase the value of the system so that it would not only score and sort the images but could also increase the commercial impact of the images to improve the conversion of the ads.…”

Section: Experiments In Real Estate Image Marketingmentioning

confidence: 99%

“…PhotoILike (PHIL) is an IQA service provided by the Spanish company with the same name. This service is based on a proprietary AI system [33] that predicts the commercial attractiveness of each image, based on human evaluations. More than a million human evaluations of real estate images were employed to build a dataset for this AI.…”

Section: Image Quality Assessmentmentioning

confidence: 99%

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Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

et al. 2022

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Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.

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Section: Experiments In Real Estate Image Marketingmentioning

confidence: 99%

Section: Image Quality Assessmentmentioning

confidence: 99%

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

et al. 2022

Self Cite

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“…The human brain tends to seek a visually harmonious experience. With an increase of digital affective information, automatic prediction of image harmony value is receiving more attention [1], however, still, there is no well-constructed theory to use as a guidance for practical purposes [2], [3], [4]. One reason for this is that aesthetic levels can vary across different domains [5].…”

Section: Introductionmentioning

confidence: 99%

Towards a Universal Understanding of Color Harmony: Fuzzy Approach

Shamoi,

Muratbekova,

Izbassar

et al. 2023

Fuzzy Systems and Data Mining IX

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Harmony level prediction is receiving increasing attention nowadays. Color plays a crucial role in affecting human aesthetic responses. In this paper, we explore color harmony using a fuzzy-based color model and address the question of its universality. For our experiments, we utilize a dataset containing attractive images from five domains: fashion, art, nature, interior design, and brand logos. Using a fuzzy approach, we aim to identify harmony patterns and dominant color palettes within these images. It is well-suited for this task because it can handle the inherent subjectivity and contextual variability associated with aesthetics and color harmony evaluation. Our experimental results suggest that color harmony is largely universal. Additionally, our findings reveal that color harmony is not solely influenced by hue relationships on the color wheel but also by the saturation and intensity of colors. In palettes with high harmony levels, we observed a prevalent adherence to color wheel principles while maintaining moderate levels of saturation and intensity. These findings contribute to ongoing research on color harmony and its underlying principles, offering valuable insights for designers, artists, and researchers in the field of aesthetics.

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“…Moreover, combination of aesthetic features with other adaptive biometric traits has a wide range of applications. Such systems can be extended to understand consumer behavior and experience, optimize the positioning of an e-commerce platform, increase the acquisition of new products, and enhance collaborative learning experience [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Three Stage Framework for Person Identification From Audio Aesthetic

Iffath

Gavrilova

2022

IEEE Access

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Social behavioral biometrics investigates social interactions to determine a person's identity. Within the discipline of social behavioral biometrics, recognition of individuals based on their aesthetic preferences is an emerging direction of research. Human aesthetic is a soft, behavioral biometric trait that refers to a person's attitudes towards a particular subject material. Recent developments in aesthetic-based biometric systems have proven that an individual's visual and audio aesthetic preferences hold considerable distinctive features. This paper introduces a novel three-stage audio-aesthetic system that can uniquely identify a user from the set of their favorite songs. The system utilizes Residual Network (ResNet) for highlevel feature extraction. A hybrid meta-heuristic feature selection algorithm based on Cuckoo Search and Whale Optimization is proposed for feature extraction optimization, which results in the low-dimensional feature set. The selected subset of features is fed into the XGBoost classifier to establish a person's identity. The proposed method outperformed the handcrafted feature-based method by achieving 99.54% accuracy on a proprietary dataset (Free Music Archive) and 99.79% accuracy on a publicly available dataset (Million Playlists Dataset).INDEX TERMS Social behavioral biometrics, deep learning, biometric authentication, audio aesthetics, transfer learning, meta-heuristic, feature selection.

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Validation of an Aesthetic Assessment System for Commercial Tasks

Cited by 7 publications

References 40 publications

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

Towards a Universal Understanding of Color Harmony: Fuzzy Approach

A Novel Three Stage Framework for Person Identification From Audio Aesthetic

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