Salience Guided Pooling in Deep Convolutional Networks

Abstract

While deep learning approach has been prevalent for generating image features, conventional handcrafted salience features still have the strength of providing explicit domain knowledge and reflecting intuitive visual understanding. However, the existing usages of handcrafted features in deep network are lack in addressing the issues of parameter quality. In this research, we propose a novel pooling method that enriches the deep features by utilizing the injected salience shape features - Generic Edge Tokens and Curve Partitioning Points, to adjust the outputs of pooling layer. The model trained under the guidance of domain prior knowledge is able to produce deep representation embracing merits from both handcrafted features and deep network. The experimental results show its improved performance with reduced learning curve. The proposed novel pooling method is generic, ie. open to other handcrafted features and different deep network architectures.