Compact Binary Fingerprint for Image Copy Re-Ranking

TL;DR

This paper introduces a compact binary fingerprint derived from SIFT features to improve image copy re-ranking, achieving better accuracy and efficiency in large-scale image retrieval tasks.

Contribution

It proposes a novel binary feature quantization of SIFT for more effective image copy detection and re-ranking.

Findings

Improved accuracy in image copy retrieval.

Reduced computational time for large datasets.

Effective false positive removal in re-ranking.

Abstract

Image copy detection is challenging and appealing topic in computer vision and signal processing. Recent advancements in multimedia have made distribution of image across the global easy and fast: that leads to many other issues such as forgery and image copy retrieval. Local keypoint descriptors such as SIFT are used to represent the images, and based on those descriptors matching, images are matched and retrieved. Features are quantized so that searching/matching may be made feasible for large databases at the cost of accuracy loss. In this paper, we propose binary feature that is obtained by quantizing the SIFT into binary, and rank list is re-examined to remove the false positives. Experiments on challenging dataset shows the gain in accuracy and time.