CropNet

Abstract

We present a deep learning-based thumbnail generation method called CropNet in this paper. Unlike previous deep learning-based methods, such as Fast-AT, which can utilize detectors introduced in object detection frameworks and generate thousands of proposals, our detector is straightforward and concise, thereby ensuring that the final cropping window is computed by its center and width, with the input aspect ratio. To achieve this goal, CropNet learns specific filters to estimate the center position and utilizes a cascade structure of filters and single neuron for width inference. In addition, CropNet optimizes the center and width jointly for optimal results. We collect a data set of more than 29,000 thumbnail annotations to train CropNet and perform cross-validation between existing data sets. Experiments show that CropNet outperforms existing techniques. Our result is achieved at a test-time speed of 17 ms per image, which is six times faster than the fastest method at present.