Improve Dog Recognition By Mining More Information From Both Click-Through Logs And Pre-Trained Models

Dog breeds recognition is a typical task of fine-grained image classification, which requires both more training images to describe each dog breed and better models to automatically discriminate different dog breeds. In this paper, we use click-through logs as source data and pre-trained deep convolutional neural network (DCNN) as initial model to build our dog recognizer. To improve recognition accuracy, we propose to mine more useful information from both data and model. Mining more information from data is achieved by mining more images for each dog breed which is achieved through automatically finding more dog-related words, while more information from pre-trained DCNNs is mined by keeping related neurons in last layer which are usually ignored in previous methods. Extensive offline experiments show consistent improvement of the proposed method. We also participate "MSR Image Recognition Challenge (IRC) @ ICME2016" under the setting of not using external data for online evaluation, our method achieves the second place comparing all methods from both tracks using and not using external data, and wins methods also not using external data by a large margin (i.e., 86.90% vs 71.35% measured in top-5 accuracy).