HCP: A Flexible CNN Framework for Multi Label Image Classification
Convolutional Neural Network (CNN) has demonstrated promising performance in single label image classification tasks. However, how CNN best copes with multi label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses CNN Pooling (HCP) where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre trained with a large scale single label image dataset, e.g. ImageNet; and 4) it may naturally output multi label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state of the arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [12] based on hand crafted features on the VOC 2012 dataset.
IEEE Transactions on Pattern Analysis and Machine Intelligence