Dunhuang Yardang National Geo-park, situated in the Gansu Province of northwestern China （40°25′36″N-40°33′10″N, 93°00′00″E-93°13′30″E）, was chosen as a research locality of aesthetics ev...Dunhuang Yardang National Geo-park, situated in the Gansu Province of northwestern China （40°25′36″N-40°33′10″N, 93°00′00″E-93°13′30″E）, was chosen as a research locality of aesthetics evaluation of yardang landforms landscape. The yardang landforms landscape is a composite structural system of patch-corridor-matrix, with four landscape unit elements as dense group, sparse group, single body and remnant. The study of the landscape aesthetics spatial pattern of Dunhuang Yardang National Geo-park shows that yardang dense group, sparse group and single body provide the greatest contribution to the aesthetic value of yardang landforms landscape. Yardang bodies are scarce, unique, irreplaceable, and priceless resources in yardang landforms areas. However, they are easily destroyed under the influence of the natural and artificial factors. Therefore, when the tourism potential of yardang landforms landscape is exploited, the protection should be fully improved.展开更多
A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale dat...A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.展开更多
基金supported by fund project from professor for "Yangtze River Scholar" of the Ministry of Education(No.801813)
文摘Dunhuang Yardang National Geo-park, situated in the Gansu Province of northwestern China （40°25′36″N-40°33′10″N, 93°00′00″E-93°13′30″E）, was chosen as a research locality of aesthetics evaluation of yardang landforms landscape. The yardang landforms landscape is a composite structural system of patch-corridor-matrix, with four landscape unit elements as dense group, sparse group, single body and remnant. The study of the landscape aesthetics spatial pattern of Dunhuang Yardang National Geo-park shows that yardang dense group, sparse group and single body provide the greatest contribution to the aesthetic value of yardang landforms landscape. Yardang bodies are scarce, unique, irreplaceable, and priceless resources in yardang landforms areas. However, they are easily destroyed under the influence of the natural and artificial factors. Therefore, when the tourism potential of yardang landforms landscape is exploited, the protection should be fully improved.
基金supported by the National Natural Science Foundation of China(Nos.61271361,61163019,61462093 and 61761046)the Research Foundation of Yunnan Province(Nos.2014FA021 and 2014FB113)the Digital Media Technology Key Laboratory of Universities in Yunnan Province
文摘A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.
