311 research outputs found

    Efficient and effective state-based framework for news video retrieval

    No full text
    In this paper, an efficient and effective framework is proposed for news video retrieval. Firstly, the 64-dimensional colour histogram is extracted as the feature vector. Then the pair quantizer is adopted to transfer the news video retrieval problem into multi-dimensional string matching problem, which conduces to the efficiency to the framework. Secondly, a new measurement named ‘optimal temporal common subsequence’, which distinguishes the difference caused by rich temporal characteristics including temporal order, temporal duration and temporal gap, is designed to match state-sequence, followed by the point & interval-based formal characterization of time-series and state-sequences. Thirdly, we tested the proposed measurement on news video retrieval. The performance shows the proposed algorithm is more effective for news video retrieval

    Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification

    Full text link
    Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicle lamps or sunlight, and existing multi-modal fusion methods are limited in their ability to recover these important cues. To address this problem, we propose a Flare-Aware Cross-modal Enhancement Network that adaptively restores flare-corrupted RGB and NIR features with guidance from the flare-immunized thermal infrared spectrum. First, to reduce the influence of locally degraded appearance due to intense flare, we propose a Mutual Flare Mask Prediction module to jointly obtain flare-corrupted masks in RGB and NIR modalities in a self-supervised manner. Second, to use the flare-immunized TI information to enhance the masked RGB and NIR, we propose a Flare-Aware Cross-modal Enhancement module that adaptively guides feature extraction of masked RGB and NIR spectra with prior flare-immunized knowledge from the TI spectrum. Third, to extract common informative semantic information from RGB and NIR, we propose an Inter-modality Consistency loss that enforces semantic consistency between the two modalities. Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation. Comprehensive experiments on both the newly collected dataset and public benchmark multi-spectral vehicle re-ID datasets demonstrate the superior performance of the proposed FACENet compared to state-of-the-art methods, especially in handling strong flares. The code and dataset will be released soon

    Character-angle based video annotation

    Get PDF
    A video annotation system includes clips organization, feature description and pattern determination. This paper aims to present a system for basketball zone-defence detection. Particularly, a character-angle based descriptor for feature description is proposed. The well-performed experimental results in basketball zone-defence detection demonstrate that it is robust for both simulations and real-life cases, with less sensitivity to the distribution caused by local translation of subprime defenders. Such a framework can be easily applied to other team-work sports

    Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

    Get PDF
    Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance

    Research progress in biological activities and mechanisms of theabrownin

    Get PDF
    Tea is beneficial to human health, which is rich in tea pigments with important biological activities. Theabrownin, derived from theaflavins and thearubigins by oxidative polymerization, mainly distributes in semi-fermented oolong tea, and completely fermented black tea and dark tea. As a kind of macromolecular substance, theabrownin cannot be directly absorbed by the gut, but it can directly interact with intestinal microbiota to regulate and maintain the homeostasis of intestinal flora. Theabrownin has multiple physiological roles via modulating the gut microbiota, including inhibiting hepatic cholesterol production, promoting the catabolism of cholesterol and triglyceride, and promoting energy metabolism in adipose tissues, thereby improving lipid metabolism. Theabrownin can also directly influence the gut absorption of glucose to improve carbohydrate metabolism and maintain blood glucose homeostasis. Theabrownin plays an anti-tumor role by inducing apoptosis and regulating gene expression in tumor cells. Theabrownin also plays an anti-inflammatory role via participating in the regulation of the immune cell differentiation and the levels of inflammatory factors. This review summarizes the formation process, the extraction procedures, and the chemical structure of theabrownin, and reviews the effects and mechanisms of theabrownin on intestinal microbiota, lipid metabolism, blood glucose homeostasis, cancer and inflammation