Visual search has been a long-standing problem in applications such as location recognition and product search. Much research has been done on image representation, matching, indexing, and retrieval. Key component tec...Visual search has been a long-standing problem in applications such as location recognition and product search. Much research has been done on image representation, matching, indexing, and retrieval. Key component technologies for visual search have been developed, and numerous real-world applications are emerging. To ensure application interoperability, the Moving Picture Experts Group （MPEG） has begun standardizing visuaJ search technologies and is developing the compact descriptors for visua） search （CDVS） standard. MPEG seeks to develop a collaborative platform for evaluating existing visual search technologies. Peking University has participated in this standardization since the 94th MPEG meeting, and significant progress has been made with the various proposals. A test model （TM） has been selected to determine the basic pipeline and key components of visual search. However, the first-version TM has high computational complexity and imperfect retrieval and matching. Core experiments have therefore been set up to improve TM. In this article, we summarize key technologies for visual search and report the progress of MPEG CDVS. We discuss Peking University＇ s efforts in CDVS and also discuss unresolved issues.展开更多
In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta ...In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram(CENTRIST)spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes.A binary and multiclass support vector machine(SVM)classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes,respectively.In this paper,we have also discussed the feature extraction methodology of several,state-of-the-art visual descriptors,and four proposed visual descriptors(Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,enhanced Ohta color histogram descriptors,and SCGWDs),in terms of experimental perspectives.The proposed enhanced Ohta color histogram descriptors,Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,SCGWD,and state-of-the-art visual descriptors were evaluated,using the Indian Institute of Technology Madras Scene Classification Image Database two,an Indoor-Outdoor Dataset,and the Massachusetts Institute of Technology indoor scene classification dataset[(MIT)-67].Experimental results showed that the indoor versus outdoor scene recognition algorithm,employing SVM with SCGWDs,produced the highest classification rates(CRs)—95.48%and 99.82%using radial basis function kernel(RBF)kernel and 95.29%and 99.45%using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets,respectively.The lowest CRs—2.08%and 4.92%,respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset.In addition,higher CRs,precision,recall,and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs,in comparison with state-of-the-art visual descriptors.展开更多
It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in de...It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.展开更多
This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement （called p2）, to mathematically model the eye fixations, p2 takes two criteria - the local consideration of sur...This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement （called p2）, to mathematically model the eye fixations, p2 takes two criteria - the local consideration of surface curvature and the global consideration of view- independent visibility - into account. Differing from existing works that often computed the intrinsic surface property of visibility in imaging space, a novel approach is proposed to approxi- mate the attribute in object space using Gauss map and Ray tracing. With the presented shape descriptor, mesh saliency detection, which refers to reasoning about which regions or points of a surface axe important, is more sensible, especially when 3D models fall into two categories： （1） the models possess significant interior/exterior structures; （2） the models contain regions where the contrast in visibility is high. For the models that are out of the categories, saliencies achieved by our approach are comparable to or even better than those of state-of-the-axt methods.展开更多
针对当前大田环境条件下对害虫进行识别研究的不足,以南方蔬菜重大害虫为研究对象,探索了一种在大田环境下使用黄色诱捕板对蔬菜害虫进行监测计数的方法。在经典图像处理算法基础上,根据害虫监测目标的需要,提出了一种基于结构化随机森...针对当前大田环境条件下对害虫进行识别研究的不足,以南方蔬菜重大害虫为研究对象,探索了一种在大田环境下使用黄色诱捕板对蔬菜害虫进行监测计数的方法。在经典图像处理算法基础上,根据害虫监测目标的需要,提出了一种基于结构化随机森林的害虫图像分割算法和利用不规则结构的特征提取算法,进一步结合背景去除、干扰目标去除和检测模型计数子算法,集成设计了基于视觉感知的蔬菜害虫计数算法(Vegetable pest counting algorithm based on visual perception,VPCA-VP)。使用了现场环境下拍摄的图像进行实验与分析,共识别出蓟马9351只,烟粉虱202只,实蝇23只。经过与人工计数比对得出,本文基于视觉感知的蔬菜害虫计数算法的平均识别正确率为94.89%。其中,蔬菜害虫蓟马的识别正确率为93.19%,烟粉虱的识别正确率为91%,实蝇的识别正确率达到100%。算法达到了较好的测试性能,可以满足害虫快速计数需求,在农田害虫监测中有一定的应用前景。展开更多
基金supported by National Basic Research "(973") Program of China(2009CB320902)the Chinese National Nature Science Foundation (60902057)
文摘Visual search has been a long-standing problem in applications such as location recognition and product search. Much research has been done on image representation, matching, indexing, and retrieval. Key component technologies for visual search have been developed, and numerous real-world applications are emerging. To ensure application interoperability, the Moving Picture Experts Group （MPEG） has begun standardizing visuaJ search technologies and is developing the compact descriptors for visua） search （CDVS） standard. MPEG seeks to develop a collaborative platform for evaluating existing visual search technologies. Peking University has participated in this standardization since the 94th MPEG meeting, and significant progress has been made with the various proposals. A test model （TM） has been selected to determine the basic pipeline and key components of visual search. However, the first-version TM has high computational complexity and imperfect retrieval and matching. Core experiments have therefore been set up to improve TM. In this article, we summarize key technologies for visual search and report the progress of MPEG CDVS. We discuss Peking University＇ s efforts in CDVS and also discuss unresolved issues.
文摘In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram(CENTRIST)spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes.A binary and multiclass support vector machine(SVM)classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes,respectively.In this paper,we have also discussed the feature extraction methodology of several,state-of-the-art visual descriptors,and four proposed visual descriptors(Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,enhanced Ohta color histogram descriptors,and SCGWDs),in terms of experimental perspectives.The proposed enhanced Ohta color histogram descriptors,Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,SCGWD,and state-of-the-art visual descriptors were evaluated,using the Indian Institute of Technology Madras Scene Classification Image Database two,an Indoor-Outdoor Dataset,and the Massachusetts Institute of Technology indoor scene classification dataset[(MIT)-67].Experimental results showed that the indoor versus outdoor scene recognition algorithm,employing SVM with SCGWDs,produced the highest classification rates(CRs)—95.48%and 99.82%using radial basis function kernel(RBF)kernel and 95.29%and 99.45%using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets,respectively.The lowest CRs—2.08%and 4.92%,respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset.In addition,higher CRs,precision,recall,and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs,in comparison with state-of-the-art visual descriptors.
基金Projects(41001260,61173122,61573380) supported by the National Natural Science Foundation of ChinaProject(11JJ5044) supported by the Hunan Provincial Natural Science Foundation of China
文摘It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.
基金Supported by China Scholarship Council(201206230015)China NSFC Key Project(61133009)the National 973 Program of China(2011CB302203)
文摘This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement （called p2）, to mathematically model the eye fixations, p2 takes two criteria - the local consideration of surface curvature and the global consideration of view- independent visibility - into account. Differing from existing works that often computed the intrinsic surface property of visibility in imaging space, a novel approach is proposed to approxi- mate the attribute in object space using Gauss map and Ray tracing. With the presented shape descriptor, mesh saliency detection, which refers to reasoning about which regions or points of a surface axe important, is more sensible, especially when 3D models fall into two categories： （1） the models possess significant interior/exterior structures; （2） the models contain regions where the contrast in visibility is high. For the models that are out of the categories, saliencies achieved by our approach are comparable to or even better than those of state-of-the-axt methods.
文摘针对当前大田环境条件下对害虫进行识别研究的不足,以南方蔬菜重大害虫为研究对象,探索了一种在大田环境下使用黄色诱捕板对蔬菜害虫进行监测计数的方法。在经典图像处理算法基础上,根据害虫监测目标的需要,提出了一种基于结构化随机森林的害虫图像分割算法和利用不规则结构的特征提取算法,进一步结合背景去除、干扰目标去除和检测模型计数子算法,集成设计了基于视觉感知的蔬菜害虫计数算法(Vegetable pest counting algorithm based on visual perception,VPCA-VP)。使用了现场环境下拍摄的图像进行实验与分析,共识别出蓟马9351只,烟粉虱202只,实蝇23只。经过与人工计数比对得出,本文基于视觉感知的蔬菜害虫计数算法的平均识别正确率为94.89%。其中,蔬菜害虫蓟马的识别正确率为93.19%,烟粉虱的识别正确率为91%,实蝇的识别正确率达到100%。算法达到了较好的测试性能,可以满足害虫快速计数需求,在农田害虫监测中有一定的应用前景。
