DSRPH: Deep semantic-aware ranking preserving hashing for efficient multi-label image retrieval

Abstract In the recent years, several hashing methods have been proposed for multi-label image retrieval. However, general methods quantify the similarities of image pairs roughly, which only consider the similarities based on category labels. In addition, general pairwise loss functions are not sensitive to the relative order of similar images. To address above problems, we present a deep semantic-aware ranking preserving hashing (DSRPH) method. First, we design a semantic-aware similarity quantization method which can measure fine-grained semantic-level similarity beyond the category based on the cosine similarity of image captions that contain high-level semantic description. Second, we propose a novel weighted pairwise loss function by adding adaptive upper and lower bounds, which can construct a compact zero-loss interval to directly constrain the relative order of similar images. Extensive experiments show that our method can generate high-quality hash codes and yield the state-of-the-art performance.