This paper presents an effective image retrieval method by combining high-level features from convolutional neural network (CNN) model and low-level features from dot-diffused block truncation coding ...(DDBTC). The low-level features, e.g., texture and color, are constructed by vector quantization -indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with the state-of-the-art methods with either low-or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.
We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location ...heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.
Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant ...differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0.
Based on Chinese industrial firms’ data, this study found that state capital injection weakened the technological progress and management efficiency of private firms, which led to a reduction in ...enterprises’ total factor productivity. State capital injection also increased the labor cost and investment in fixed assets, and lowered the profitability of firms. Furthermore, the state capital did not have a significant negative impact on firms in technology-intensive and monopoly industries. This study has shed some lights on the reform of the state-owned assets management system and the development of a mixed ownership economy.
Terahertz waves are expected to be used in next-generation communications, detection, and other fields due to their unique characteristics. As a basic part of the terahertz application system, the ...terahertz detector plays a key role in terahertz technology. Due to the two-dimensional structure, graphene has unique characteristics features, such as exceptionally high electron mobility, zero band-gap, and frequency-independent spectral absorption, particularly in the terahertz region, making it a suitable material for terahertz detectors. In this review, the recent progress of graphene terahertz detectors related to photovoltaic effect (PV), photothermoelectric effect (PTE), bolometric effect, and plasma wave resonance are introduced and discussed.
Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification ...is challenging due to many factors. One of the difficulties is the label constraint. Specifically, each case is simply labeled the glioma subtype without precise annotations of lesion regions information. In this paper, we propose a novel hybrid fully convolutional neural network (CNN)-based method for glioma subtype classification using both whole slide imaging (WSI) and multiparametric magnetic resonance imagings (mpMRIs). It is comprised of two methods: a WSI-based method and a mpMRIs-based method. For the WSI-based method, we categorize the glioma subtype using a 2D CNN on WSIs. To overcome the label constraint issue, we extract the truly representative patches for the glioma subtype classification in a weakly supervised fashion. For the mpMRIs-based method, we develop a 3D CNN-based method by analyzing the mpMRIs. The mpMRIs-based method consists of brain tumor segmentation and classification. Finally, to enhance the robustness of the predictions, we fuse the WSI-based and mpMRIs-based results guided by a confidence index. The experimental results on the validation dataset in the competition of CPM-RadPath 2020 show the comprehensive judgments from both two modalities can achieve better performance than the ones by solely using WSI or mpMRIs. Furthermore, our result using the proposed method ranks the third place in the CPM-RadPath 2020 in the testing phase. The proposed method demonstrates a competitive performance, which is creditable to the success of weakly supervised approach and the strategy of label agreement from multi-modality data.
Underwater images suffer from serious color distortion and detail loss because of the wavelength-dependent light absorption and scattering, which seriously influences the subsequent underwater object ...detection and recognition. The latest methods for underwater image enhancement are based on deep models, which focus on finding a mapping function from the underwater image subspace to a ground-truth image subspace. They neglect the diversity of underwater conditions which leads to different background colors of underwater images. In this paper, we propose a Class-condition Attention Generative Adversarial Network (CA-GAN) to enhance an underwater image. We build an underwater image dataset which contains ten categories generated by the simulator with different water attenuation coefficient and depth. Relying on the underwater image classes, CA-GAN creates a many-to-one mapping function for an underwater image. Moreover, in order to generate the realistic image, attention mechanism is utilized. In the channel attention block, the feature maps in the front-end layers and the back-end layers are fused along channels, and in the spatial attention block, feature maps are pixel-wise fused. Extensive experiments are conducted on synthetic and real underwater images. The experimental results demonstrate that CA-GAN can effectively recover color and detail of various scenes of underwater images and is superior to the state-of-the-art methods.
Discriminative correlation filter (DCF) based tracking algorithms have obtained prominent speed and accuracy strengths, which have attracted extensive attention and research. However, some ...unavoidable deficiencies still exist. For example, the circulant shifted sampling process is likely to cause repeated periodic assumptions and cause boundary effects, which degrades the tracker’s discriminative performance, and the target is not easy to locate in complex appearance changes. In this paper, a spatial–temporal regularization module based on BACF (background-aware correlation filter) framework is proposed, which is performed by introducing a temporal regularization to deal effectively with the boundary effects issue. At the same time, the accuracy of target recognition is improved. This model can be effectively optimized by employing the alternating direction multiplier (ADMM) method, and each sub-problem has a corresponding closed solution. In addition, in terms of feature representation, we combine traditional hand-crafted features with deep convolution features linearly enhance the discriminative performance of the filter. Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100.
Floodplain wetlands are among the most dynamic ecosystems on Earth, featuring high biodiversity and productivity. They are also sensitive to anthropogenic disturbances and are globally threatened. ...Understanding how flow regime drives the spatiotemporal dynamics of wetland habitats is fundamental to effective conservation practices. In this study, using Landsat imagery and the random forest (RF) machine learning algorithm, we mapped the winter distribution of four wetland habitats (i.e., Carex meadow, reedbed, mudflat, and shallow water) in East Dongting Lake, a Ramsar wetland in the middle to lower Yangtze Basin of China, for 34 years (1988–2021). The dynamics of wetland habitats were explored through pixel-by-pixel comparisons. Further, the response of wetland habitats to flow regime variations was investigated using generalized additive mixed models (GAMM). Our results demonstrated the constant expansion of reedbeds and shrinkage of mudflats, and that there were three processes contributing to the reduction in mudflat: (1) permanent replacement by reedbed; (2) irreversible loss to water; and (3) transitional swapping with Carex meadow. These changes in the relative extent of wetland habitats may degrade the conservation function of the Ramsar wetland. Moreover, the duration of the dry season and the date of water level withdrawal were identified as the key flow regime parameters shaping the size of wetland habitats. However, different wetland vegetation showed distinct responses to variations in flow regime: while Carex meadow increased with earlier water withdrawal and a longer dry season, reedbed continuously expanded independent of the flow regime corresponding to the increase in winter rainfall. Our findings suggested that flow regime acts in concert with other factors, such as climate change and sand mining in river channels, driving wetland habitat transition in a floodplain landscape. Therefore, effective conservation can only be achieved through diverse restoration strategies addressing all drivers.
Scaly-sided Merganser is an endangered species confined to eastern Asia, primarily breeding in southeast Russia and northeast China. Its breeding range is quite large, with limited information on ...population dynamics. The Kievka River catchment in far east Russia is one of the most densely populated breeding grounds, providing the only extensive, long-term monitoring on breeding Scaly-sided Merganser. This study assessed the current status of Scaly-sided Merganser and investigated the environmental factors influencing the population. From 2014–2021, the population size at each river reach fluctuated with no directional trends, while the density of Scaly-sided Merganser increased. The result suggested a higher concentration of breeding Scaly-sided Merganser in Kievka River catchment in recent years, particularly preferring downstream area. The species also demonstrated an affinity to less riparian vegetation and higher flow rates. Notably, river flow rate emerges as a potential key issue for designing environmental flows of the breeding habitat for Scaly-sided Merganser, presenting a challenge for researchers and managers.
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