Low Level Features for Quality Assessment of Facial Images

Lienhard, Arnaud; Ladret, Patricia; Caplier, Alice

doi:10.5220/0005308805450552

Cited by 15 publications

(14 citation statements)

References 17 publications

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“…The classification accuracy [5,6,13,14,68,73,75,78,84,[87][88][89][90][91][92][93][94][95][102][103][104][105][106], which reports the proportion of the results that are correctly classified.…”

Section: Evaluation Metrics and Comparison Of The Methodsmentioning

confidence: 99%

“…The Euclidean distance between the ground truth and aesthetics ratings [76,105,107,108] and the correlation ranking [77,82,87] are used for evaluating performances in regression tasks.…”

mentioning

confidence: 99%

“…Finally, the Receiver-Operating Characteristic (ROC) curve [79,105,[109][110][111] and area under the curve [81,101,109] concern the performance of binary classifiers when the discrimination threshold is varied.…”

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confidence: 99%

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Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning Techniques

Bodini

2019

Inventions

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Although the concept of image quality has been a subject of study for the image processing community for more than forty years (where, with the term “quality”, we are referring to the accuracy with which an image processing system captures, processes, stores, compresses, transmits, and displays the signals that compose an image), notions related to aesthetics of photographs and images have only appeared for about ten years within the community. Studies devoted to aesthetics of images are multiplying today, taking advantage of the latest machine learning techniques and mostly due to the proliferation of huge communities and websites, specialized in digital photography sharing and archiving, such as Flickr, Imgur, DeviantArt, and Instagram. In this review, we examine the latest advances of computer methods that aim at computationally distinguishing high-quality from low-quality photos and images, relying on machine learning techniques. The paper is organized as follows: First, we introduce many approaches to aesthetics, studied in philosophy, neurobiology, experimental psychology, and sociology, to see what lighting they propose to researchers. Such points of view let us explain the weakness of the current consensus on the difficult aesthetics problem and the importance of the ongoing debates on it. Then, we analyze the work done in the community of pattern recognition and artificial intelligence on the task of automatic aesthetic assessment, and we both compare and critically examine the presented results. Finally, we describe many issues that have not been addressed, and starting from these, we outline some possible future directions.

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Section: Evaluation Metrics and Comparison Of The Methodsmentioning

confidence: 99%

“…The Euclidean distance between the ground truth and aesthetics ratings [76,105,107,108] and the correlation ranking [77,82,87] are used for evaluating performances in regression tasks.…”

mentioning

confidence: 99%

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Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning Techniques

Bodini

2019

Inventions

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“…Literature Main idea and contributions Global and regional features Ke et al [15](2006) -Modeling the global aesthetic properties of photos Sun et al [56](2009) -Using global saliency map to estimate visual attention distribution Aydin et al [57](2015) -Computing meaning aesthetic signatures to predict the aesthetic score Luo et al [16] The Visual attention distribution in the saliency map [56,67] Signatures in sharpness, exposure, colorfulness, tone, clarity, and depth [57] Regional clarity contrast, lighting, simplicity, composition geometry [16] Composition, skyillumination, and scene content attributes [17,38] Time, scene geolocation, envrionmental condition, image type [58] Face-specific features (e.g. expression, pose, distance) [69,59] The SIFT and color statistics features encoded in BOV and FV [60,70] Bag-of-color-patterns [61] Bag-of-aesthetic-preserving features [62] Fitness in color templates of Matsuda color coordination [39] Self-similarity, complexity, anisotropy, aspect ratio [63] LAB-based color and SIFTbased texture visual words [40,73,64] Ink shading, the ratio of wet and dry ink brush strokes, white space distribution [51,65] FIGURE 10. Convention approaches with handcrafted features.…”

Section: Typementioning

confidence: 99%

A Comprehensive Survey on Computational Aesthetic Evaluation of Visual Art Images: Metrics and Challenges

Zhang

Miao

2021

IEEE Access

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Computational image aesthetic evaluation is a computable human aesthetic perception and judgment realized by machines, which has a significant impact on a variety of applications such as image advanced search and promotional exhibition of painting arts. Various approaches have been proposed in copious literature trying to solve this challenging problem. However, there have been few attempts in reviewing works from different types of visual arts, due to their significant differences in visual features and aesthetic principles. In this survey, we present a comprehensive listing of the reviewed works on aesthetic assessment of photographs and paintings, mainly highlighting the contributions and innovations of the existing approaches. We firstly introduce aesthetic assessment benchmark datasets in different categories. Then, conventional aesthetic evaluation approaches based on handcrafted features are reviewed. Besides, we systematically evaluate recent deep learning techniques that are useful for developing robust models for aesthetic prediction tasks in scoring, distribution, attribute, and description. Moreover, the possibility of aesthetic-aware color enhancement, recomposition of photo images, and automatic generation of aestheticguided art paintings through computational approaches are summarized. Finally, challenges and potential future directions for this field are discussed. We hope that our survey could serve as a comprehensive reference source for future research on computational aesthetics in visual media.

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“…Lienhard et al [71] study particular face features for evaluating the aesthetic quality of headshot images. To design features for face/headshots, the input image is divided into sub-regions (eyes region, mouth region, global face region and entire image region).…”

Section: Task-specific Featuresmentioning

confidence: 99%

Image Aesthetic Assessment: An experimental survey

Deng

Loy

Tang

2017

IEEE Signal Process. Mag.

243

170

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Abstract-This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality photos from low-quality ones based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature trying to solve this challenging problem. In this survey, we present a systematic listing of the reviewed approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (dataset characteristics and evaluation metrics). Main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep learning techniques, we systematically evaluate recent deep learning settings that are useful for developing a robust deep model for aesthetic scoring. Experiments are conducted using simple yet solid baselines that are competitive with the current state-of-the-arts. Moreover, we discuss the possibility of manipulating the aesthetics of images through computational approaches. We hope that our survey could serve as a comprehensive reference source for future research on the study of image aesthetic assessment.

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Low Level Features for Quality Assessment of Facial Images

Cited by 15 publications

References 17 publications

Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning Techniques

Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning Techniques

A Comprehensive Survey on Computational Aesthetic Evaluation of Visual Art Images: Metrics and Challenges

Image Aesthetic Assessment: An experimental survey

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