Packing and Padding: Coupled Multi-index for Accurate Image Retrieval

TL;DR

This paper introduces a coupled Multi-Index framework that fuses SIFT and color features at the indexing level to improve image retrieval accuracy and efficiency, significantly reducing false positives.

Contribution

The novel coupled Multi-Index method effectively combines multiple features at indexing, enhancing discriminative power and retrieval performance over traditional single-feature approaches.

Findings

Significantly improves retrieval accuracy on benchmark datasets.

Reduces false positive matches in image retrieval.

Consumes only half the query time compared to baseline methods.

Abstract

In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.