Unsupervised Semantic Scene Labeling for Streaming Data
Unsupervised Semantic Scene Labeling for Streaming Data
复制标题
流数据的无监督语义场景标记
DOI：
10.1109/cvpr.2017.626
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Rogers
中科院分区：
文献类型：
--
作者：
Maggie B. Wigness;J. Rogers
我们引入了一种无监督的语义场景标记方法，该方法不断学习和适应数据流中发现的语义模型。虽然与无监督视频分割密切相关，但我们的算法并不是设计用于产生连贯过分割的早期视频处理策略，而是直接学习更高级别的语义概念。这是通过基于集成的方法实现的，其中每个学习器从数据流中的局部窗口聚类数据。重叠的局部窗口被处理并编码在图结构中，以创建跨窗口的标签映射并协调标签以减少无监督学习噪声。此外，我们迭代地从观察到的数据相似性中学习合并阈值标准，以自动确定学习标签的数量，而无需人工提供参数。实验表明，我们的方法语义标签的视频流具有很高的准确度，并实现了更好的平衡下，过分割熵比现有的视频分割算法给出类似数量的标签输出。
We introduce an unsupervised semantic scene labeling approach that continuously learns and adapts semantic models discovered within a data stream. While closely related to unsupervised video segmentation, our algorithm is not designed to be an early video processing strategy that produces coherent over-segmentations, but instead, to directly learn higher-level semantic concepts. This is achieved with an ensemble-based approach, where each learner clusters data from a local window in the data stream. Overlapping local windows are processed and encoded in a graph structure to create a label mapping across windows and reconcile the labelings to reduce unsupervised learning noise. Additionally, we iteratively learn a merging threshold criteria from observed data similarities to automatically determine the number of learned labels without human provided parameters. Experiments show that our approach semantically labels video streams with a high degree of accuracy, and achieves a better balance of under and over-segmentation entropy than existing video segmentation algorithms given similar numbers of label outputs.