A new scheme named personalized image retrieval technique based on visual perception is proposed in this letter, whose motive is to narrow the semantic gap by directly perceiving user's visual information. It uses...A new scheme named personalized image retrieval technique based on visual perception is proposed in this letter, whose motive is to narrow the semantic gap by directly perceiving user's visual information. It uses visual attention model to segment image regions and eye-tracking technique to record fixations. Visual perception is obtained by analyzing the fixations in regions to measure gaze interests. Integrating visual perception into attention model is to detect the Regions Of Interest (ROIs), whose features are extracted and analyzed, then feedback interests to optimize the results and construct user profiles.展开更多
The earliest description of Cai Lun is that of an inventor of papermaking,which is included in historical books such as Dong Guan Han Ji and The Book of the Later Han;later,in his hometown,he was also inked by scholar...The earliest description of Cai Lun is that of an inventor of papermaking,which is included in historical books such as Dong Guan Han Ji and The Book of the Later Han;later,in his hometown,he was also inked by scholars because of some monuments about him,or the authors used his experience to express their own emotions.Recently,Cai Lun’s image has been clarified in textbooks,literary works,and urban civilization construction and is no longer considered merely a pioneer of papermaking.The development of his historical and literary image derives not only from the need for academic research but also the need to establish the Chinese nationalism and patriotism and promote traditional culture.展开更多
Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived pref...Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.展开更多
基金Supported by the National Natural Science Foundation of China (No.60472036, No.60431020, No.60402036)the Natural Science Foundation of Beijing (No.4042008)and Ph.D. Foundation of Ministry of Education (No.20040005015).
文摘A new scheme named personalized image retrieval technique based on visual perception is proposed in this letter, whose motive is to narrow the semantic gap by directly perceiving user's visual information. It uses visual attention model to segment image regions and eye-tracking technique to record fixations. Visual perception is obtained by analyzing the fixations in regions to measure gaze interests. Integrating visual perception into attention model is to detect the Regions Of Interest (ROIs), whose features are extracted and analyzed, then feedback interests to optimize the results and construct user profiles.
基金supported by the Anhui Provincial Quality Engineering Project of Colleges and Universities(2022jyxm1833,2019jyxm0014,2014jyxm015)Anhui Province New Era Education Quality Engineering Project(2022szsfkc010,2022jyjxggyj031,2022jyjxggyj030)University of Science and Technology of China Teaching Research Project(2023xjyxm060,2022xjyxm009,2022ychx10,2022ycjg14,2020kcsz062,2021ycjg12,2021kcsz035,2023xkcszkc08).
文摘The earliest description of Cai Lun is that of an inventor of papermaking,which is included in historical books such as Dong Guan Han Ji and The Book of the Later Han;later,in his hometown,he was also inked by scholars because of some monuments about him,or the authors used his experience to express their own emotions.Recently,Cai Lun’s image has been clarified in textbooks,literary works,and urban civilization construction and is no longer considered merely a pioneer of papermaking.The development of his historical and literary image derives not only from the need for academic research but also the need to establish the Chinese nationalism and patriotism and promote traditional culture.
基金supported partially by the National Key Research and Development Program of China(2018YFB1004903)National Natural Science Foundation of China(61802453,U1911401,U1811461)+1 种基金Fundamental Research Funds for the Central Universities(19lgpy216)Research Projects of Zhejiang Lab(2019KD0AB03).
文摘Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.
