The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Kaiqi Huang - One of the best experts on this subject based on the ideXlab platform.

  • from classification to regression model transfer for visual Aesthetic Quality assessment
    Asian Conference on Pattern Recognition, 2017
    Co-Authors: Wenzhen Huang, Peipei Yang, Kaiqi Huang
    Abstract:

    Visual Aesthetic Quality assessment has played an important role in increasing number of computer vision applications. Particularly, estimating the Quality score precisely is a main task of Aesthetic Quality assessment, but the training samples labeled with score are usually expensive to obtain. In this paper, we propose a transfer learning method which can improve the performance of Aesthetic score prediction by using the coarse labeled samples, which are much easier to obtain. The proposed method incorporates the coarse information from source domain into the target domain by a novel multi-task framework, which can revise the model in target task. The effectiveness of our method is proven by experimental results that the error is reduced obviously with the help of source domain.

  • deep Aesthetic Quality assessment with semantic information
    IEEE Transactions on Image Processing, 2017
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    Human beings often assess the Aesthetic Quality of an image coupled with the identification of the image’s semantic content. This paper addresses the correlation issue between automatic Aesthetic Quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of Aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the Aesthetic task. In particular, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging Aesthetic Visual Analysis dataset and Photo.net dataset validate the importance of semantic recognition in Aesthetic Quality assessment, and demonstrate that multitask deep models can discover an effective Aesthetic representation to achieve the state-of-the-art results.

  • automatic image cropping with Aesthetic map and gradient energy map
    International Conference on Acoustics Speech and Signal Processing, 2017
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    Image cropping is a fundamental task in image editing to enhance the Aesthetic Quality of images. In this paper, we propose an automatic image cropping technique based on Aesthetic map and gradient energy map. Instead of utilizing Aesthetic rules in previous methods, we learn the Aesthetic map by a deep convolutional neural network with a large-scale dataset for Aesthetic Quality assessment. The Aesthetic map can highlight the discriminative image regions for high (or low) Aesthetic Quality category. The gradient energy map presents edge spatial distribution of images and is developed to compute the simplicity of images. Then a composition model is learned with the Aesthetic map and gradient energy map to evaluate the Quality of composition for crops. Moreover, an Aesthetic preservation model is developed to compute the Aesthetic information remained in crops to avoid cropping out high Aesthetic regions. Experiments show that our approach significantly outperforms state-of-the-art cropping methods.

  • hierarchical Aesthetic Quality assessment using deep convolutional neural networks
    Signal Processing-image Communication, 2016
    Co-Authors: Yueying Kao, Kaiqi Huang, Steve Maybank
    Abstract:

    Aesthetic image analysis has attracted much attention in recent years. However, assessing the Aesthetic Quality and assigning an Aesthetic score are challenging problems. In this paper, we propose a novel framework for assessing the Aesthetic Quality of images. Firstly, we divide the images into three categories: "scene", "object" and "texture". Each category has an associated convolutional neural network (CNN) which learns the Aesthetic features for the category in question. The object CNN is trained using the whole images and a salient region in each image. The texture CNN is trained using small regions in the original images. Furthermore, an A&C CNN is developed to simultaneously assess the Aesthetic Quality and identify the category for overall images. For each CNN, classification and regression models are developed separately to predict Aesthetic class (high or low) and to assign an Aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can outperform the state-of-the-art methods for each category. HighlightsPropose an Aesthetic Quality assessment framework by dividing images into 3 categories.Three specific CNNs and an A&C CNN are constructed for Aesthetic features learning.Different models for each CNN are developed to predict Aesthetic class and score.

  • deep Aesthetic Quality assessment with semantic information
    arXiv: Computer Vision and Pattern Recognition, 2016
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    Human beings often assess the Aesthetic Quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic Aesthetic Quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of Aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the Aesthetic task. Particularly, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging AVA dataset and this http URL dataset validate the importance of semantic recognition in Aesthetic Quality assessment, and demonstrate that multi-task deep models can discover an effective Aesthetic representation to achieve state-of-the-art results.

Chusong Chen - One of the best experts on this subject based on the ideXlab platform.

  • video Aesthetic Quality assessment by temporal integration of photo and motion based features
    IEEE Transactions on Multimedia, 2013
    Co-Authors: Hsinho Yeh, Chunyu Yang, Mingsui Lee, Chusong Chen
    Abstract:

    This paper presents a new method for accessing the Aesthetic Quality of videos. It consists of two processes: Aesthetic features construction and temporal integration. First, our method combines both photo-based and motion-based visual clues to extract the Aesthetic features for each frame in a video. We introduce new motion-based features built from optical flow and salient region extraction, and show their effectiveness to enhance the estimation of Aesthetic values. Then, a temporal-order-aware framework that integrates the frame-based features is presented to further improve the evaluation accuracy by taking the time-varying properties into consideration. The experimental results demonstrate that our approach can accomplish remarkable improvement for Aesthetic Quality assessment of videos.

  • intelligent photographing interface with on device Aesthetic Quality assessment
    International Conference on Computer Vision, 2012
    Co-Authors: Kenghao Liu, Chusong Chen
    Abstract:

    This paper proposes a efficient method for instant photo Aesthetics Quality assessment that can be implemented on general portable devices. The classification performance is guaranteed to 0.89 on benchmark photo database. We also port our method onto a middle-level tablet computer to execute instantly and we find it reaches good acceptable efficiency. Moreover, an Aesthetic information display to present the Aesthetics evaluation results to users is introduced.

  • video Aesthetic Quality assessment by combining semantically independent and dependent features
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Chunyu Yang, Hsinho Yeh, Chusong Chen
    Abstract:

    This paper aims to accomplish the work of assessing the Aesthetic Quality of a video. Unlike previous assessing works focusing mainly on the extraction of Aesthetic features in a film, we further study the features, discover their semantic property on videos and then come up with more useful video-based features such as motion space and motion direction entropy. In the experiment, we compare the assessing accuracy between two different semantic types of features and find that the semantic-independent feature is more reliable from the results. By combining all features, our method learned a more robust and accurate assessment model.

Yueying Kao - One of the best experts on this subject based on the ideXlab platform.

  • deep Aesthetic Quality assessment with semantic information
    IEEE Transactions on Image Processing, 2017
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    Human beings often assess the Aesthetic Quality of an image coupled with the identification of the image’s semantic content. This paper addresses the correlation issue between automatic Aesthetic Quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of Aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the Aesthetic task. In particular, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging Aesthetic Visual Analysis dataset and Photo.net dataset validate the importance of semantic recognition in Aesthetic Quality assessment, and demonstrate that multitask deep models can discover an effective Aesthetic representation to achieve the state-of-the-art results.

  • automatic image cropping with Aesthetic map and gradient energy map
    International Conference on Acoustics Speech and Signal Processing, 2017
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    Image cropping is a fundamental task in image editing to enhance the Aesthetic Quality of images. In this paper, we propose an automatic image cropping technique based on Aesthetic map and gradient energy map. Instead of utilizing Aesthetic rules in previous methods, we learn the Aesthetic map by a deep convolutional neural network with a large-scale dataset for Aesthetic Quality assessment. The Aesthetic map can highlight the discriminative image regions for high (or low) Aesthetic Quality category. The gradient energy map presents edge spatial distribution of images and is developed to compute the simplicity of images. Then a composition model is learned with the Aesthetic map and gradient energy map to evaluate the Quality of composition for crops. Moreover, an Aesthetic preservation model is developed to compute the Aesthetic information remained in crops to avoid cropping out high Aesthetic regions. Experiments show that our approach significantly outperforms state-of-the-art cropping methods.

  • hierarchical Aesthetic Quality assessment using deep convolutional neural networks
    Signal Processing-image Communication, 2016
    Co-Authors: Yueying Kao, Kaiqi Huang, Steve Maybank
    Abstract:

    Aesthetic image analysis has attracted much attention in recent years. However, assessing the Aesthetic Quality and assigning an Aesthetic score are challenging problems. In this paper, we propose a novel framework for assessing the Aesthetic Quality of images. Firstly, we divide the images into three categories: "scene", "object" and "texture". Each category has an associated convolutional neural network (CNN) which learns the Aesthetic features for the category in question. The object CNN is trained using the whole images and a salient region in each image. The texture CNN is trained using small regions in the original images. Furthermore, an A&C CNN is developed to simultaneously assess the Aesthetic Quality and identify the category for overall images. For each CNN, classification and regression models are developed separately to predict Aesthetic class (high or low) and to assign an Aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can outperform the state-of-the-art methods for each category. HighlightsPropose an Aesthetic Quality assessment framework by dividing images into 3 categories.Three specific CNNs and an A&C CNN are constructed for Aesthetic features learning.Different models for each CNN are developed to predict Aesthetic class and score.

  • deep Aesthetic Quality assessment with semantic information
    arXiv: Computer Vision and Pattern Recognition, 2016
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    Human beings often assess the Aesthetic Quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic Aesthetic Quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of Aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the Aesthetic task. Particularly, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging AVA dataset and this http URL dataset validate the importance of semantic recognition in Aesthetic Quality assessment, and demonstrate that multi-task deep models can discover an effective Aesthetic representation to achieve state-of-the-art results.

  • visual Aesthetic Quality assessment with multi task deep learning
    arXiv: Computer Vision and Pattern Recognition, 2016
    Co-Authors: Yueying Kao, Kaiqi Huang
    Abstract:

    This paper considers the problem of assessing visual Aesthetic Quality with semantic information. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition offers the key to addressing this problem. Based on convolutional neural networks, we propose a general multi-task framework with four different structures. In each structure, Aesthetic Quality assessment task and semantic recognition task are leveraged, and different features are explored to improve the Quality assessment. Moreover, an effective strategy of keeping a balanced effect between the semantic task and Aesthetic task is developed to optimize the parameters of our framework. The correlation analysis among the tasks validates the importance of the semantic recognition in Aesthetic Quality assessment. Extensive experiments verify the effectiveness of the proposed multi-task framework, and further corroborate the above proposition.

Bin Zhou - One of the best experts on this subject based on the ideXlab platform.

  • deep multimodality learning for uav video Aesthetic Quality assessment
    IEEE Transactions on Multimedia, 2020
    Co-Authors: Qi Kuang, Xin Jin, Qinping Zhao, Bin Zhou
    Abstract:

    Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the Aesthetics of aerial videos that can provide valuable information for improving the Aesthetic Quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video Aesthetic Quality assessment. More specifically, a multistream framework is designed to exploit Aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video Aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and Aesthetic-based UAV path planning using the proposed method.

Qi Kuang - One of the best experts on this subject based on the ideXlab platform.

  • deep multimodality learning for uav video Aesthetic Quality assessment
    IEEE Transactions on Multimedia, 2020
    Co-Authors: Qi Kuang, Xin Jin, Qinping Zhao, Bin Zhou
    Abstract:

    Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the Aesthetics of aerial videos that can provide valuable information for improving the Aesthetic Quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video Aesthetic Quality assessment. More specifically, a multistream framework is designed to exploit Aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video Aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and Aesthetic-based UAV path planning using the proposed method.

  • Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment
    'Institute of Electrical and Electronics Engineers (IEEE)', 2020
    Co-Authors: Qi Kuang, Jin Xin, Zhao Qinping, Zhou Bin
    Abstract:

    Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the Aesthetics of aerial videos that can provide valuable information for improving the Aesthetic Quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video Aesthetic Quality assessment. More specifically, a multistream framework is designed to exploit Aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video Aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and Aesthetic-based UAV path planning using the proposed method.Comment: IEEE Trans. on Multimedia, 202