An Image Retrieval Method Based on Weighting Aggregation of Deep Convolutional Features

As the current image retrieval methods based on deep convolutional features cannot fully highlight salient regional features of images and cannot effectively suppress background noises, an efficient unsupervised image retrieval method based on the weighting aggregation of deep convolutional features is proposed.According to the inverse document frequency, this method assigns a larger channel weighting to the feature map with fewer features and compact features and then generates a differentiated weighting vector.As different images have different features, this method selects a set of feature maps that can most truly indicate image features, calculates the weighting matrix, and performs a filtering process to generate a selected filter weighting matrix.Experimental results on different public datasets show that the proposed method can effectively improve the discrimination power of image features.Furthermore, under the same experimental setting, the proposed method is superior to other similar aggregation approaches on image retrieval accuracy.