Correlation Hashing Network for Efficient Cross-Modal Retrieval

02/22/2016
by   Yue Cao, et al.
0

Hashing is widely applied to approximate nearest neighbor search for large-scale multimodal retrieval with storage and computation efficiency. Cross-modal hashing improves the quality of hash coding by exploiting semantic correlations across different modalities. Existing cross-modal hashing methods first transform data into low-dimensional feature vectors, and then generate binary codes by another separate quantization step. However, suboptimal hash codes may be generated since the quantization error is not explicitly minimized and the feature representation is not jointly optimized with the binary codes. This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error. The proposed CHN is a hybrid deep architecture that constitutes a convolutional neural network for learning good image representations, a multilayer perception for learning good text representations, two hashing layers for generating compact binary codes, and a structured max-margin loss that integrates all things together to enable learning similarity-preserving and high-quality hash codes. Extensive empirical study shows that CHN yields state of the art cross-modal retrieval performance on standard benchmarks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/15/2022

Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization

Cross-modal retrieval aims to search for data with similar semantic mean...
research
04/30/2019

Effective and Efficient Indexing in Cross-Modal Hashing-Based Datasets

To overcome the barrier of storage and computation, the hashing techniqu...
research
01/14/2020

Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval

Due to the superiority in similarity computation and database storage fo...
research
12/17/2021

Nearest neighbor search with compact codes: A decoder perspective

Modern approaches for fast retrieval of similar vectors on billion-scale...
research
02/02/2017

HashNet: Deep Learning to Hash by Continuation

Learning to hash has been widely applied to approximate nearest neighbor...
research
08/08/2017

Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval

Cross-modal hashing is usually regarded as an effective technique for la...
research
12/25/2020

Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing

Unsupervised cross-modal hashing (UCMH) has become a hot topic recently....

Please sign up or login with your details

Forgot password? Click here to reset