Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
Liang Zheng, Shengjin Wang, Ziqiong Liu, Qi Tian

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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Multimodal Machine Learning Applications
