Image retrieval using dual-weighted deep feature descriptor.

Applying deep convolutional features to image retrieval has become the mainstream method in the field of image retrieval. However, the discriminative power of deep convolutional features needs to be further improved. So far, low-level features have not been used to enhance representation. Furthermore, the compatibility of low-level features and deep features remains challenging. To address these problems, we propose an aggregation method that combines low-level features and deep features for image retrieval. The highlights are: (1) We propose a novel similarity weight to complement the advantages of the intrinsic information in each feature type, thereby improving the discriminative power of deep convolutional features. (2) We introduce a feature loss weight to calculate the diversity between texture features and deep features, which effectively utilizes the complementary advantages of each feature type. (3) We propose a novel representation for image retrieval, named the dual-weighted deep feature descriptor. It not only strengthens the discriminative power of the intrinsic information within feature maps, but also avoids the spatial weighting from mistakenly removing useful information. Experiments demonstrate that our method outperforms some state-of-the-art methods on five benchmark datasets. It is not only simpler to implement, as it does not require complex processes such as retraining a convolutional neural network model, but also has low experimental requirements. [ABSTRACT FROM AUTHOR]