3 research outputs found
Semi-supervised Hashing for Semi-Paired Cross-View Retrieval
Recently, hashing techniques have gained importance in large-scale retrieval
tasks because of their retrieval speed. Most of the existing cross-view
frameworks assume that data are well paired. However, the fully-paired
multiview situation is not universal in real applications. The aim of the
method proposed in this paper is to learn the hashing function for semi-paired
cross-view retrieval tasks. To utilize the label information of partial data,
we propose a semi-supervised hashing learning framework which jointly performs
feature extraction and classifier learning. The experimental results on two
datasets show that our method outperforms several state-of-the-art methods in
terms of retrieval accuracy.Comment: 6 pages, 5 figures, 2 table
Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving
The measure between heterogeneous data is still an open problem. Many
research works have been developed to learn a common subspace where the
similarity between different modalities can be calculated directly. However,
most of existing works focus on learning a latent subspace but the semantically
structural information is not well preserved. Thus, these approaches cannot get
desired results. In this paper, we propose a novel framework, termed
Cross-modal subspace learning via Kernel correlation maximization and
Discriminative structure-preserving (CKD), to solve this problem in two
aspects. Firstly, we construct a shared semantic graph to make each modality
data preserve the neighbor relationship semantically. Secondly, we introduce
the Hilbert-Schmidt Independence Criteria (HSIC) to ensure the consistency
between feature-similarity and semantic-similarity of samples. Our model not
only considers the inter-modality correlation by maximizing the kernel
correlation but also preserves the semantically structural information within
each modality. The extensive experiments are performed to evaluate the proposed
framework on the three public datasets. The experimental results demonstrated
that the proposed CKD is competitive compared with the classic subspace
learning methods.Comment: The paper is under consideration at Multimedia Tools and Application
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features
Hashing techniques have been applied broadly in retrieval tasks due to their
low storage requirements and high speed of processing. Many hashing methods
based on a single view have been extensively studied for information retrieval.
However, the representation capacity of a single view is insufficient and some
discriminative information is not captured, which results in limited
improvement. In this paper, we employ multiple views to represent images and
texts for enriching the feature information. Our framework exploits the
complementary information among multiple views to better learn the
discriminative compact hash codes. A discrete hashing learning framework that
jointly performs classifier learning and subspace learning is proposed to
complete multiple search tasks simultaneously. Our framework includes two
stages, namely a kernelization process and a quantization process.
Kernelization aims to find a common subspace where multi-view features can be
fused. The quantization stage is designed to learn discriminative unified
hashing codes. Extensive experiments are performed on single-label datasets
(WiKi and MMED) and multi-label datasets (MIRFlickr and NUS-WIDE) and the
experimental results indicate the superiority of our method compared with the
state-of-the-art methods.Comment: 28 pages, 10 figures, 13 tables. The paper is under consideration at
Pattern Analysis and Application