Assessing photo quality with geo-context and crowdsourced photos

Yin, Wenyuan; Mei, Tao; Chen, Chang Wen

doi:10.1109/vcip.2012.6410821

Cited by 17 publications

(13 citation statements)

References 7 publications

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“…Yin et al [73] build a scene-dependent aesthetic model by incorporating the geographic location information with GIST descriptors and spatial layout of saliency features for scene aesthetic classification (such as bridges, mountains and beaches). SVM is used as the classifier.…”

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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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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“…[1]. In image retrieval scenario, search engines are expected to retrieve images not only by content relevance but also by aesthetic quality [2].…”

Section: Introductionmentioning

confidence: 99%

“…Beside, some approaches were also proposed to improve the system accuracy from other aspects, such as using user comments [21], building new large-scale image database [9], or using geo-context information [2], and so on. These encouraging results have led to several prototypes for assessing image esthetics.…”

Section: Introductionmentioning

confidence: 99%

An efficient image aesthetic analysis system using Hadoop

Wang

Zhao

Cai

et al. 2015

Signal Processing: Image Communication

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“…Tang et al divided images into seven categories according to their content and adopted different assessment s- [11], [12]. Though this can reduce the difficulty of photo quality assessment, in real applications, it is still a big problem to identify specific kind of images automatically and precisely.…”

Section: Introductionmentioning

confidence: 99%

Effective and efficient photo quality assessment

Dong

Tian

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Automatic photo quality assessment from the perspective of visual aesthetics is a hot research topic due to its potential need in numerous applications. It tries to automatically determine whether a given image has "high" or "low" quality according to the image's visual content. Most existing researches in photo quality assessment predominantly focus on exploring hand-crafted features which may be potentially related to highlevel aesthetic attributes. Most of those features are designed under the guidance of some common photography rules and prior knowledge. However, due to the subjectivity and complexity of humans' aesthetic activities, automatic image aesthetic quality assessment is very challenging. Those features are not effective enough and show varying performance on different datasets. Besides, they often require high computational cost. In this paper, we propose a set of compact aesthetic features which are not only effective but also highly efficient. We test those features on two large scale real world image datasets. The experimental results demonstrate that the proposed features achieve the best performance consistently over different datasets with a much lower computational complexity.

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Assessing photo quality with geo-context and crowdsourced photos

Cited by 17 publications

References 7 publications

Image Aesthetic Assessment: An experimental survey

Image Aesthetic Assessment: An experimental survey

An efficient image aesthetic analysis system using Hadoop

Effective and efficient photo quality assessment

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