Places: An Image Database for Deep Scene Understanding
This provides a high-coverage, high-diversity dataset to tackle intractable visual recognition problems in computer vision, though it is incremental as it builds on existing dataset initiatives.
The authors introduced the Places Database, a collection of 10 million labeled scene photographs, to address the need for large-scale datasets in deep scene understanding, achieving impressive baseline performances using Convolutional Neural Networks.
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world. Using state of the art Convolutional Neural Networks, we provide impressive baseline performances at scene classification. With its high-coverage and high-diversity of exemplars, the Places Database offers an ecosystem to guide future progress on currently intractable visual recognition problems.