Variable-Length Metric Learning for Fast Image Retrieval

Abstract

Learning a powerful distance metric is the key component of image retrieval. Recently, deep metric learning has been an active research topic for image retrieval. However, most existing metric learning approaches treat all the input images equally and learn all image embeddings at equal lengths. These methods ignore those easy examples that can be encoded as the shorter features, which is search-inefficient. We propose a simple but efficient variable-length metric learning method for search efficiency, in which the different query samples have different feature-length. First, we propose to learn the ranked (prioritized) list of features. The more distinguishing features, the higher the rank. We show that the proposed prioritized features can be used to perform fast retrieval in different feature-length configurations. Further, we propose an adaptive feature-length selection policy to determine the amount of feature-length for each query sample. Extensive experiments are conducted on three benchmark datasets. The results demonstrate that the proposed method can reduce computational costs without incurring a decrease in accuracy.