Image retrieval by aggregating deep orientation structure features

Aggregating deep features for image retrieval has shown excellent performance in terms of accuracy. However, exploring visual perception properties to activate the dormant discriminative cues of deep convolutional feature maps has received little attention. To address this issue, we present a novel representation, namely the deep orientation aggregation histogram, to image retrieval via aggregating deep orientation structure features. Its main highlights are: (1) A statistical orientation computation model is proposed to detect candidate directions. It will help to use the feature maps to exploit various orientation to provide robust representation. (2) A computed module is proposed to active the discriminative orientation cues hidden in the deep convolutional feature maps. It can boost the representation of deep features with aid of the statistical orientation and their orientation structures. (3) The proposed method can stimulate orientation-selectivity mechanism to provide a strong discriminative yet compact representation. Experimental results on five popular benchmark datasets demonstrated that the proposed method could improve retrieval performance in terms of mAP scores. Furthermore, it outperforms some existing state-of-the-art methods without complex fine-tuning. The proposed method benefits to retrieve the scene images with various color and direction details.