ILSVRC on a Smartphone

In this work, to the best of our knowledge, we propose a stand-alone large-scale image classification system running on an Android smartphone. The objective of this work is to prove that mobile large-scale image classification requires no communication to external servers. To do that, we propose a scalar-based compression method for weight vectors of linear classifiers. As an additional characteristic, the proposed method does not need to uncompress the compressed vectors for evaluation of the classifiers, which brings the saving of recognition time. We have implemented a large-scale image classification system on an Android smartphone, which can perform 1000class classification for a given image in 0.270 seconds. In the experiment, we show that compressing the weights to 1/8 leaded to only 0.80% performance loss for 1000-class classification with the ILSVRC2012 dataset. In addition, the experimental results indicate that weight vectors compressed in low bits, even in the binarized case (bit=1), are still valid for classification of high dimensional vectors.