Sketch-based image retrieval (SBIR) has been extensively studied for decades because sketch is one of the most intuitive ways to describe ideas. However, the large expressional gap between hand-drawn sketches and natu...
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Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was eff...
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Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.
In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity. The proposed framework mainly has thr...
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Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimension...
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Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.
Music history, referring to the records of users' listening or downloading history in online music services, is the primary source for music service providers to analyze users' preferences on music and thus to...
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Plastic film mulching (PM), which contributes to nearly half of the increased crop yields in dryland agroecosystems, exacerbates environmental burdens due to its non-degradable nature. Globally, there is a growing dem...
Plastic film mulching (PM), which contributes to nearly half of the increased crop yields in dryland agroecosystems, exacerbates environmental burdens due to its non-degradable nature. Globally, there is a growing demand to replace non-degradable PM with degradable film mulching (DM), yet its impacts on soil organic carbon (SOC) in dryland agroecosystems remains unknown. Here, using multi-field studies and mesocosm experiments, we found that DM strongly increased but PM reduced SOC storage (0-1 m). This difference is likely attributable to the higher microbial C use efficiency in soil under DM, leading to increased microbial-derived C compared to PM. Under the high roading scenario for 2100, DM could reduce the decomposition of SOC (0-1 m) in China's drylands by 9.0 ± 1.0 Mg ha year (one standard error) compared with PM. Our findings highlight that DM is a promising alternative to PM for sequestrating SOC and alleviating C loss under climate change in dryland agroecosystems.
We study the problem of recognizing sign language automatically using the RGB videos and skeleton coordinates captured by Kinect, which is of great significance in communication between the deaf and the hearing societ...
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In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tas...
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ISBN:
(纸本)9781467388511
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two subnetworks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other stateof-the-art image recommendation solutions.
Our research has revealed a hidden relationship among several basic components, which leads to the best target detection result. Further, we have proved that the matched filter (MF) is always superior to the constrain...
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—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, met...
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