A multi-scene deep learning model for image aesthetic evaluation

Wang, Weining; Zhao, Mingquan; Wang, Li; Huang, Jiexiong; Cai, Chengjia; Xu, Xiangmin

doi:10.1016/j.image.2016.05.009

Cited by 82 publications

(67 citation statements)

References 15 publications

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“…Before deep learning era, many hand-crafted features [7,16] are designed for aesthetic image classification and scoring as surveyed by Deng et al [9]. Deep learning methods are proposed recently for aesthetic assessment [11,14,15,17,18,20,21,23,24,31]. They outperform traditional methods.…”

Section: Related Workmentioning

confidence: 99%

Aesthetic Attributes Assessment of Images

Jin

Zhao

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

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Figure 1: Aesthetic Attributes Assessment of Images. We predict caption and score of each aesthetic attribute of an image.ABSTRACT Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named DPC-Captions which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions.

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Section: Related Workmentioning

confidence: 99%

Aesthetic Attributes Assessment of Images

Jin

Zhao

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

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“…Architectural enhancement is done in different ways such as RGB, Depth [24][36], optical flow [37] information is treated as different channels and fed to single column CNN or mutilcolumns CNN is trained using different data(global and local patches). Some architectural modification over alexnet is used in [38] to [39]. For example in [38], the is obtained by fusing conv5 F I and conv5 F O .…”

Section: Featuresmentioning

confidence: 99%

Scene perception system for visually impaired based on object detection and classification using multimodal deep convolutional neural network

Kaur

Bhattacharya

2019

J. Electron. Imag.

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This paper represents a cost-effective scene perception system aimed towards visually impaired individual. We use an odroid system integrated with an USB camera and USB laser that can be attached on the chest. The system classifies the detected objects along with its distance from the user and provides a voice output. Experimental results provided in this paper use outdoor traffic scenes.The object detection and classification framework exploits a multi-modal fusion based faster RCNN using motion, sharpening and blurring filters for efficient feature representation.

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“…Dong et al [16] proposed to adopt the generic features from the penultimate layer output of AlexNet with spatial pyramid pooling. Wang et al [17] proposed a CNN modified from AlexNet by stacking seven scene convolutional layers. Jin et al [11] proposed ILGNet derived from part of the GoogLeNet which contains Inception module.…”

Section: Image Aesthetics Assessmentmentioning

confidence: 99%

“…The content and scenery of an image are fundamental to its aesthetics and are sometimes overlooked in the assessment. Also, it's been proved that taking image content into account can improve the accuracy of image aesthetics prediction [14,17].…”

Section: Deep Neural Network For Computer Vision Tasksmentioning

confidence: 99%

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Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

Jia

Fan

2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common largescale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.

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A multi-scene deep learning model for image aesthetic evaluation

Cited by 82 publications

References 15 publications

Aesthetic Attributes Assessment of Images

Aesthetic Attributes Assessment of Images

Scene perception system for visually impaired based on object detection and classification using multimodal deep convolutional neural network

Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

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