Paper Info
Title
Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval
Abstract
The last few years have witnessed the rise of the big data era, in which approximate nearest neighbor search is a fundamental problem in many applications, such as large-scale image retrieval. Recently, many research results demonstrate that hashing can achieve promising performance due to its appealing storage and search efficiency. Since the complex optimization problems for loss functions are difficult to solve, most hashing methods decompose the hash codes learning problem into two steps: projection and quantization. In the quantization step, binary codes are widely used because ranking them by Hamming distance is very efficient. However, the huge information loss produced by the quantization step should be reduced in applications, such as image retrieval where high search accuracy is required. Since many two-step hashing methods produce uneven projected dimensions in the projection step, in this paper, we propose a novel dimension analysis based quantization method (DAQ) on two-step hashing methods for image retrieval. We first perform an importance analysis of the projected dimensions and select a subset of them that are more informative than the others, then we divide the selected projected dimensions into several regions with our quantizer. Every region is quantized with its corresponding codebook. Finally, the similarity between two hash codes is estimated by Manhattan distance between their corresponding codebooks, which is also efficient. We conduct experiments on three public benchmarks containing up to one million descriptors and show that the proposed DAQ method consistently leads to significant accuracy improvements over state-of-the-art quantization methods.
Year
DOI
Venue
2021
10.1109/TMM.2020.3033118
IEEE TRANSACTIONS ON MULTIMEDIA
Keywords
DocType
Volume
Quantization (signal), Data acquisition, Image retrieval, Nearest neighbor methods, Hamming distance, Symmetric matrices, Binary codes, Approximate nearest neighbor search, hashing algorithms, image retrieval, quantization
Journal
23
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yuan Cao1112.86
Heng Qi221830.45
Jie Gui370025.72
Keqiu Li41415162.02
Yuan Yan Tang52662209.20
James T. Kwok64920312.83