A Sparse Ensemble Learning System For Efficient Semantic Indexing

This demo presents an extremely efficient concept detection system based on a novel bag of words extraction method and sparse ensemble learning. We will show that the presented system can efficiently build the concept detectors upon millions of images, and achieve real-time concept detection on unseen images with the state-of-the-arts accuracy. To do so, we first develop an efficient bag of visual words (BoW) construction method based on sparse non-negative matrix factorization (NMF) and GPU enabled SIFT feature extraction. We then develop a sparse ensemble learning method to build the detection model, which drastically reduces learning time in order of magnitude over traditional methods like Support Vector Machine. The demo video of the system is available at YouTube: http://youtu.be/57obnlCxqAs