<div style="text-align:justify;"> An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the u...<div style="text-align:justify;"> An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and colour similarity. Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR). This paper discusses and describes about the colour features technique for image retrieval systems. Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features. This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features. </div>展开更多
The colour feature is often used in the object tracking.The tracking methods extract the colour features of the object and the background,and distinguish them by a classifier.However,these existing methods simply use ...The colour feature is often used in the object tracking.The tracking methods extract the colour features of the object and the background,and distinguish them by a classifier.However,these existing methods simply use the colour information of the target pixels and do not consider the shape feature of the target,so that the description capability of the feature is weak.Moreover,incorporating shape information often leads to large feature dimension,which is not conducive to real-time object tracking.Recently,the emergence of visual tracking methods based on deep learning has also greatly increased the demand for computing resources of the algorithm.In this paper,we propose a real-time visual tracking method with compact shape and colour feature,which forms low dimensional compact shape and colour feature by fusing the shape and colour characteristics of the candidate object region,and reduces the dimensionality of the combined feature through the Hash function.The structural classification function is trained and updated online with dynamic data flow for adapting to the new frames.Further,the classification and prediction of the object are carried out with structured classification function.The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark dataset OTB-100 and OTB-13.展开更多
The measurement of crop nutrition is considerably significant in agricultural practices,especially in the application of mechanized variable rate fertilization.Feature extraction and model building are two important l...The measurement of crop nutrition is considerably significant in agricultural practices,especially in the application of mechanized variable rate fertilization.Feature extraction and model building are two important links in crop nutrition measurement by digital image.In this paper,a feature set of fusion multi-colour space in field prototype is extracted and an evaluation approach using stepwise-based ridge regression(SBRR)that uses correlation-based evaluation method is employed.First the image features of three known colour spaces are extracted,meanwhile a new colour space named rgb is constructed according to the characteristics that RGB colour space easily affected by light.Then the SBRR with nested cross validation is used to find the best evaluation model.By performance evaluation,the optimal SBRR model is obtained(R^(2)=0.718 RMSE=5.111).Additionally,compared with two other nutritional evaluation approach named backpropagation artificial neural network(BP-ANN)and k-nearest neighbors(KNN),SBRR achieves better performance in both R^(2) and RMSE.Furthermore the proposed model’s reliability is verified using the image dataset taken from the same wheat field in the next year.The R^(2) and RMSE values are 0.794 and 4.304,respectively.The comparisons and verification show that our proposed SBRR approach can achieve better experimental results and can be considered a reliable and low-cost alternative for estimating the chlorophyll content of wheat leaves in field.展开更多
文摘<div style="text-align:justify;"> An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and colour similarity. Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR). This paper discusses and describes about the colour features technique for image retrieval systems. Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features. This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features. </div>
基金This work was supported by the National Key Research and Development Plan(No.2016YFC0600908)the National Natural Science Foundation of China(No.61772530,U1610124)+1 种基金Natural Science Foundation of Jiangsu Province of China(No.BK20171192)China Postdoctoral Science Foundation(No.2016T90524,No.2014M551696).
文摘The colour feature is often used in the object tracking.The tracking methods extract the colour features of the object and the background,and distinguish them by a classifier.However,these existing methods simply use the colour information of the target pixels and do not consider the shape feature of the target,so that the description capability of the feature is weak.Moreover,incorporating shape information often leads to large feature dimension,which is not conducive to real-time object tracking.Recently,the emergence of visual tracking methods based on deep learning has also greatly increased the demand for computing resources of the algorithm.In this paper,we propose a real-time visual tracking method with compact shape and colour feature,which forms low dimensional compact shape and colour feature by fusing the shape and colour characteristics of the candidate object region,and reduces the dimensionality of the combined feature through the Hash function.The structural classification function is trained and updated online with dynamic data flow for adapting to the new frames.Further,the classification and prediction of the object are carried out with structured classification function.The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark dataset OTB-100 and OTB-13.
基金The National Key Research and Development Program of China,China(2016YFD0200403)The Graduate Innovation Funding Project of Hebei Province,China(CXZZBS2017059)+1 种基金Scientific Science and Technology Research Projects of Universities in Hebei,China(BJ2018012)Project of Hebei Natural Science Foundation,China(G2018204093).
文摘The measurement of crop nutrition is considerably significant in agricultural practices,especially in the application of mechanized variable rate fertilization.Feature extraction and model building are two important links in crop nutrition measurement by digital image.In this paper,a feature set of fusion multi-colour space in field prototype is extracted and an evaluation approach using stepwise-based ridge regression(SBRR)that uses correlation-based evaluation method is employed.First the image features of three known colour spaces are extracted,meanwhile a new colour space named rgb is constructed according to the characteristics that RGB colour space easily affected by light.Then the SBRR with nested cross validation is used to find the best evaluation model.By performance evaluation,the optimal SBRR model is obtained(R^(2)=0.718 RMSE=5.111).Additionally,compared with two other nutritional evaluation approach named backpropagation artificial neural network(BP-ANN)and k-nearest neighbors(KNN),SBRR achieves better performance in both R^(2) and RMSE.Furthermore the proposed model’s reliability is verified using the image dataset taken from the same wheat field in the next year.The R^(2) and RMSE values are 0.794 and 4.304,respectively.The comparisons and verification show that our proposed SBRR approach can achieve better experimental results and can be considered a reliable and low-cost alternative for estimating the chlorophyll content of wheat leaves in field.
