| Summary: | © 2017 IEEE. Deep residual networks have recently emerged as the state-of-the-art architecture in image classification and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of the deep residual networks pre-trained on ImageNet. We propose to use ResFeats for diverse image classification tasks namely, object classification, scene classification and coral classification and show that ResFeats consistently perform better than their CNN counterparts on these classification tasks. Since the ResFeats are large feature vectors, we explore dimensionality reduction methods. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features.
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