Inverted Semantic-Index for Image Retrieval
Ying Wang

TL;DR
This paper introduces an inverted semantic-index for large-scale image retrieval that leverages image classification for partitioning, combined with product quantization, to improve retrieval accuracy and candidate list quality.
Contribution
It replaces traditional unsupervised clustering with image classification in index construction and proposes a merging-splitting method to adapt the number of partitions.
Findings
Significantly improved retrieval accuracy on benchmarks.
Enhanced candidate list quality with semantic-aware indexing.
Effective combination of semantic index with product quantization.
Abstract
This paper addresses the construction of inverted index for large-scale image retrieval. The inverted index proposed by J. Sivic brings a significant acceleration by reducing distance computations with only a small fraction of the database. The state-of-the-art inverted indices aim to build finer partitions that produce a concise and accurate candidate list. However, partitioning in these frameworks is generally achieved by unsupervised clustering methods which ignore the semantic information of images. In this paper, we replace the clustering method with image classification, during the construction of codebook. We then propose a merging and splitting method to solve the problem that the number of partitions is unchangeable in the inverted semantic-index. Next, we combine our semantic-index with the product quantization (PQ) so as to alleviate the accuracy loss caused by PQ…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Multimodal Machine Learning Applications
