In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static datas...In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static dataset clustering. Conventional methods rely on the statistical information of previous clusters to improve the efficiency of incremental clustering;thus, error accumulation may occur. Therefore, this study proposes to predict the summaries of previous data directly from data distribution via supervised learning. Moreover, an efficient framework to cluster previous summaries with new data is explored. Although learning summaries from original data costs more than those from previous clusters, the entire framework consumes just a little bit more time because clustering current data and generating summaries for new data share most of the calculations. Experiments show that the proposed approach significantly outperforms the existing incremental face clustering methods, as evidenced by the improvement of average F-score from 0.644 to 0.762. Compared with state-of-the-art static face clustering methods, our method can yield comparable accuracy while consuming much less time.展开更多
为解决图像分类任务中模型结构固化、产生巨大内存消耗、时间消耗的问题,提出一种增量式深度神经网络(IDNN)。输入样本通过聚类算法激活不同簇并被分别处理:如果新样本激活已有簇,则更新该簇参数;否则为新簇开辟分支,并训练独立特征集。...为解决图像分类任务中模型结构固化、产生巨大内存消耗、时间消耗的问题,提出一种增量式深度神经网络(IDNN)。输入样本通过聚类算法激活不同簇并被分别处理:如果新样本激活已有簇,则更新该簇参数;否则为新簇开辟分支,并训练独立特征集。在Caltech-101、ORL Face、ETH-80数据库的验证结果表明,该系统能自动调整网络结构,适用于轮廓、纹理、视角等不同环境的增量式学习,例如在Caltech-101库分类任务中准确率超出VGGNet 5.08%、Alex Net 3.44%。展开更多
基金supported by the National Natural Science Foundation of China (Nos. 61701277 and 61771288)the State Key Development Program in13th Five-Year (Nos. 2016YFB0801301, 044007008, and 2016YFB1001005)supported by the National Engineering Laboratory for Intelligent Video Analysis and Application of China。
文摘In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static dataset clustering. Conventional methods rely on the statistical information of previous clusters to improve the efficiency of incremental clustering;thus, error accumulation may occur. Therefore, this study proposes to predict the summaries of previous data directly from data distribution via supervised learning. Moreover, an efficient framework to cluster previous summaries with new data is explored. Although learning summaries from original data costs more than those from previous clusters, the entire framework consumes just a little bit more time because clustering current data and generating summaries for new data share most of the calculations. Experiments show that the proposed approach significantly outperforms the existing incremental face clustering methods, as evidenced by the improvement of average F-score from 0.644 to 0.762. Compared with state-of-the-art static face clustering methods, our method can yield comparable accuracy while consuming much less time.
文摘为解决图像分类任务中模型结构固化、产生巨大内存消耗、时间消耗的问题,提出一种增量式深度神经网络(IDNN)。输入样本通过聚类算法激活不同簇并被分别处理:如果新样本激活已有簇,则更新该簇参数;否则为新簇开辟分支,并训练独立特征集。在Caltech-101、ORL Face、ETH-80数据库的验证结果表明,该系统能自动调整网络结构,适用于轮廓、纹理、视角等不同环境的增量式学习,例如在Caltech-101库分类任务中准确率超出VGGNet 5.08%、Alex Net 3.44%。
