Transfer Learning based Multi-label Classification of Images
Khushboo Anand · International Journal for Research in Applied Science and Engineering Technology · 2019
Multi-label classification task is concerned with classifying an image into one or more classes(categories) based on the content of the image itself. Multi-label classification is different from binary or multi-class classification wherein the aim of the classifier built is to classify the image into a single class from a set number of classes. Existing methods utilize feature extraction techniques such as colour histograms, SIFT which are limited by their representational ability. We propose to overcome this problem by leveraging the rich features that can be extracted from CNN that have been trained on million images. The features are then fed into an Artificial Neural Net, which is trained on the image features and multi-label tags. By utilising transfer learning, we harness the feature representational ability combined with reduced training time. We benchmark the model with dataset obtained from Flickr (FLICKR-25K). The evaluation metrics utilised here include mAP, Training accuracy and Training Loss.