Ultra-High-Definition Aerial Photo Categorization by an Enhanced Matrix Factorization Algorithm

In this work, we designed an effective ultra-high-definition (UHD) aerial photo categorization pipeline by designing an enhanced deep multi-clue matrix factorization (DMCMF). In detail, given a UHD aerial photo, those visually salient ground objects are extracted in the first place. In order to explicitly encode their spatial layout, multiple graphlets are constructed in each UHD aerial photo. Each is built by connecting those spatially neighboring object patches. Afterward, we propose a new matrix factorization (MF) model that intelligently uncover the underlying semantic features from graphlets. And multiple informative clues are encoded into the MF model. Notably, our DMCMF is optimized progressively. And we can represent each graphlet by a vector of binary hash codes. Lastly, each UHD aerial photograph can be effectively quantized into a feature vector by a kernel machine for multi-label categorization. Experiments have shown that our method is highly competitive in learning categorization model from imperfect labels at image-level.