Product Quantization to Reduce Entropy of Labels for Fast and Accurate Image Retrieval

Product quantization (PQ) is a popular technique for fast image retrieval from a large-scale database. PQ methods quantize image features into short codes and realize fast retrieval using lookup tables based on the codes. Although the entropy of labels (i.e., ground truths for retrieval) is crucial for the retrieval performance, existing PQ methods focus only on the quantization errors. This paper proposes a novel PQ method that reduces the entropy of labels to improve the retrieval performance. We assume that correct labels for each training sample are known; then, we train the codes so that we can minimize the label errors as well as the quantization errors to reduce the entropy of labels. This enables fast and accurate retrieval when queries (i.e., images whose labels are unknown) are given.

Automated summary

Product quantization (PQ) is a popular technique for fast image retrieval, but existing methods focus only on quantization errors. This paper proposes a novel PQ method that reduces the entropy of labels to improve retrieval performance, enabling fast and accurate retrieval when queries are given.