SIFT Meets CNN: A Decade Survey of Instance Retrieval

TL;DR

This survey reviews a decade of instance retrieval methods, comparing SIFT-based and CNN-based approaches, analyzing their performance, and discussing future research directions in the field.

Contribution

It provides a comprehensive overview of both SIFT and CNN-based instance retrieval methods, categorizing them by codebook size and CNN strategy, and analyzing their evolution and performance.

Findings

CNN-based methods show impressive performance improvements.

SIFT and CNN methods have different strengths and application scenarios.

Hybrid approaches offer promising results in instance retrieval.

Abstract

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.