In this paper, we present a novel scalable logo recognition system which can recognize a large number of logo categories locally on mobile devices. The system is unsupervised without any supervised training procedure, and very time efficient at low memory cost. It is also robust against challenging conditions such as noise addition, different image scale, rotation, etc. To achieve this goal, we propose an efficient segmental quantization approach for generation of large visual words over one million size with a very compact vocabulary. The vocabulary consists of two small dictionaries learned through sparse non-negative matrix factorization (NMF) of local SIFT descriptors. With an inverted index structure built through the large visual words, query images containing logos can be recognized through efficient retrieval of K-nearest neighbors (K-NN) of logo instances in the dataset. Our vocabulary size is very small, only one thousandth of that of traditional Approximate K-Means (AKM) method, which is of great importance for mobile devices with limited memory. Furthermore, based on the compact dictionary, we present a promising verification way of filtering false positives via sparse reconstruction of SIFT descriptors with a very few number of sparse codes due to the sparsity's property of lowest reconstruction error. Experiments on our dataset with 400 logo classes show that our system is very efficient and effective.
['Sheng Tang', 'Yongdong Zhang', 'Hui Chen']
Scalable logo recognition based on compact sparse dictionary for mobile devices