维护道路交叉口信息的现势性和完整性是城市道路地图生产和更新的关键环节。针对众源轨迹数据存在的时空异质性和密度分布不均等问题,提出了一种道路交叉口层次化提取方法。首先对原始轨迹数据进行自适应密度匀化处理;然后结合车辆轨迹...维护道路交叉口信息的现势性和完整性是城市道路地图生产和更新的关键环节。针对众源轨迹数据存在的时空异质性和密度分布不均等问题,提出了一种道路交叉口层次化提取方法。首先对原始轨迹数据进行自适应密度匀化处理;然后结合车辆轨迹方向、速度变化与转弯距离比等多维特征提取交叉口轨迹;再借助具有噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法将交叉口轨迹聚类为不同交叉口目标,进而通过轨迹起始点航向编码进行交叉口内部通行模式提取;最后利用分段轨迹迭代连接方法提取交叉口骨架结构。结果表明,该方法能准确完整地提取不同形状、不同尺度的道路交叉口结构,提取精度和效率较密度匀化前有较大提升。展开更多
Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognitio...Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process （AHP） is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation （membership function） is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.展开更多
In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific...In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.展开更多
文摘维护道路交叉口信息的现势性和完整性是城市道路地图生产和更新的关键环节。针对众源轨迹数据存在的时空异质性和密度分布不均等问题,提出了一种道路交叉口层次化提取方法。首先对原始轨迹数据进行自适应密度匀化处理;然后结合车辆轨迹方向、速度变化与转弯距离比等多维特征提取交叉口轨迹;再借助具有噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法将交叉口轨迹聚类为不同交叉口目标,进而通过轨迹起始点航向编码进行交叉口内部通行模式提取;最后利用分段轨迹迭代连接方法提取交叉口骨架结构。结果表明,该方法能准确完整地提取不同形状、不同尺度的道路交叉口结构,提取精度和效率较密度匀化前有较大提升。
文摘Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process （AHP） is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation （membership function） is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.
基金Project supported by the National Natural Science Foundation of China(No.61379074)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ12F02003 and LY15F020035)
文摘In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.
