Adaptive Query Re-ranking Based on ImageGraph for Image Retrieval

With the exponential growth of images, the accuracy of image retrieval for improving the performance of rank layer and feature layer in content-based image retrieval (CBIR) becomes more and more attractive. Better single feature search results will further enhance the effect. Better sorting results will robust the performance. Therefore, this paper focuses on rank reordering to improve the performance of image retrieval. We propose a rank-level framework for feature reordering based on hierarchical undirected graphs. First, we calculate the K Nearest Neighbors for each image in the database. Then, we propose a method based on the combination of reciprocal nearest neighbor and K nearest neighbor to construct an undirected graph, and a method for calculating the image distance based on the shared nearest neighbor. An undirected graph is used to represent the similarity relationship of an image, where the vertices are composed of pictures and the edges are weighted according to the distance of the pictures. Finally, based on the constructed undirected graph in this paper, we perform sorting optimization. Experiments on four public data sets demonstrate the effectiveness of the method and are challenging.