The priority of the filled patch play a key role in the exemplar-based image inpainting, and it should be determined firstly to optimize the process of image inpainting. A modified image inpainting algorithm is propos...The priority of the filled patch play a key role in the exemplar-based image inpainting, and it should be determined firstly to optimize the process of image inpainting. A modified image inpainting algorithm is proposed by weighted-priority based on the Criminisi algorithm. The improved algorithm demonstrates better relationship between the data term and the confidence term for the optimization of the priority than the classical Criminisi algorithm. By comparing the effect of the inpainted images with different structure, conclusion can be drawn that the optimal priority should be chosen properly for different images with different structures.展开更多
Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which l...Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.展开更多
In this paper, we present machine learning algorithms and systems for similar video retrieval. Here, the query is itself a video. For the similarity measurement, exemplars, or representative frames in each video, are ...In this paper, we present machine learning algorithms and systems for similar video retrieval. Here, the query is itself a video. For the similarity measurement, exemplars, or representative frames in each video, are extracted by unsupervised learning. For this learning, we chose the order-aware competitive learning. After obtaining a set of exemplars for each video, the similarity is computed. Because the numbers and positions of the exemplars are different in each video, we use a similarity computing method called M-distance, which generalizes existing global and local alignment methods using followers to the exemplars. To represent each frame in the video, this paper emphasizes the Frame Signature of the ISO/IEC standard so that the total system, along with its graphical user interface, becomes practical. Experiments on the detection of inserted plagiaristic scenes showed excellent precision-recall curves, with precision values very close to 1. Thus, the proposed system can work as a plagiarism detector for videos. In addition, this method can be regarded as the structuring of unstructured data via numerical labeling by exemplars. Finally, further sophistication of this labeling is discussed.展开更多
China has developed its own views on human rights and pursued its own path of human rights development in light of the UN Charter principles,its national conditions,and its experience over many years.Human rights in C...China has developed its own views on human rights and pursued its own path of human rights development in light of the UN Charter principles,its national conditions,and its experience over many years.Human rights in China have scored remarkable achievements.The Chinese perspective on human rights and the Chinese path of human rights development has become a shining example for the whole world.展开更多
Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealisti...Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealistic results.In this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style.The proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic map.We then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically accurate.Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.展开更多
Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques eInphasize creation of optimal target textures, and pay insufficient attention to choi...Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques eInphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, of taining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant post- processing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly erop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured t）atches from non-textured patches, in conjunction with SVM prediction. To identify real texture, exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure （using t）inary Gabor pattern （BGP）） and dominant color features （using color histograms） between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures mauually selected by an artist and a statistical study demonstrate its effectiveness.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60972106)Postdoctoral Science Foundation (No. 20090450750)the Science Foundation of Tianjin(No. 11JCYBJC00900)
文摘The priority of the filled patch play a key role in the exemplar-based image inpainting, and it should be determined firstly to optimize the process of image inpainting. A modified image inpainting algorithm is proposed by weighted-priority based on the Criminisi algorithm. The improved algorithm demonstrates better relationship between the data term and the confidence term for the optimization of the priority than the classical Criminisi algorithm. By comparing the effect of the inpainted images with different structure, conclusion can be drawn that the optimal priority should be chosen properly for different images with different structures.
基金supported by Research Team Development Funds of L.Xue and Z.H.Ouyang,Electronic Countermeasure Institute,National University of Defense Technology。
文摘Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.
文摘In this paper, we present machine learning algorithms and systems for similar video retrieval. Here, the query is itself a video. For the similarity measurement, exemplars, or representative frames in each video, are extracted by unsupervised learning. For this learning, we chose the order-aware competitive learning. After obtaining a set of exemplars for each video, the similarity is computed. Because the numbers and positions of the exemplars are different in each video, we use a similarity computing method called M-distance, which generalizes existing global and local alignment methods using followers to the exemplars. To represent each frame in the video, this paper emphasizes the Frame Signature of the ISO/IEC standard so that the total system, along with its graphical user interface, becomes practical. Experiments on the detection of inserted plagiaristic scenes showed excellent precision-recall curves, with precision values very close to 1. Thus, the proposed system can work as a plagiarism detector for videos. In addition, this method can be regarded as the structuring of unstructured data via numerical labeling by exemplars. Finally, further sophistication of this labeling is discussed.
文摘China has developed its own views on human rights and pursued its own path of human rights development in light of the UN Charter principles,its national conditions,and its experience over many years.Human rights in China have scored remarkable achievements.The Chinese perspective on human rights and the Chinese path of human rights development has become a shining example for the whole world.
基金supported in part by National Natural Science Foundation of China(U21B2023)DEGP Innovation Team(2022KCXTD025)+1 种基金Shenzhen Science and Technology Program(KQTD20210811090044003,RCJC20200714114435012)Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ).
文摘Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealistic results.In this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style.The proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic map.We then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically accurate.Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.
基金supported in part by grants from the National Natural Science Foundation of China(Nos.61303101 and 61572328)the Shenzhen Research Foundation for Basic Research,China(Nos.JCYJ20150324140036846,JCYJ20170302153551588,CXZZ20140902160818443,CXZZ20140902102350474,CXZZ20150813151056544,JCYJ20150630105452814,JCYJ20160331114551175,and JCYJ20160608173051207)the Startup Research Fund of Shenzhen University(No.2013-827-000009)
文摘Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques eInphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, of taining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant post- processing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly erop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured t）atches from non-textured patches, in conjunction with SVM prediction. To identify real texture, exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure （using t）inary Gabor pattern （BGP）） and dominant color features （using color histograms） between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures mauually selected by an artist and a statistical study demonstrate its effectiveness.
