Image Retrieval Using Multi-layer Orientation Histograms

Abstract Various levels of feature maps extracted from the convolutional neural network models can capture multiple degrees of semantic cues in representation. However, learning the relationship between different semantic features across numerous layers can be challenging. Furthermore, existing representations cannot effectively capture the orientation cues. This paper proposes a representation method, the multi-layer orientation histogram, to address these problems. The main highlights are: (1) An iterative multi-layer integration method to combine the feature maps of various levels is suggested in this study. This method can provide discriminative characteristics based on spatial relationships. (2) An effective approach is suggested to apply Gabor filtering for detecting orientation cues. It can amplify the most dominant orientation cues and is convenient for efficiently using them in subsequent implementations. (3) The proposed representation directly captures orientation cues from each learned feature map. It can incorporate the learned deep features and orientation cues to create a more discriminative representation. Comparative experiments demonstrate that the proposed method exhibits a highly competitive performance on several benchmark datasets in terms of mean average precision. Moreover, this method can provide an effective architecture for learning discriminative global features.