2 research outputs found
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
Survey on Visual Sentiment Analysis
Visual Sentiment Analysis aims to understand how images affect people, in
terms of evoked emotions. Although this field is rather new, a broad range of
techniques have been developed for various data sources and problems, resulting
in a large body of research. This paper reviews pertinent publications and
tries to present an exhaustive overview of the field. After a description of
the task and the related applications, the subject is tackled under different
main headings. The paper also describes principles of design of general Visual
Sentiment Analysis systems from three main points of view: emotional models,
dataset definition, feature design. A formalization of the problem is
discussed, considering different levels of granularity, as well as the
components that can affect the sentiment toward an image in different ways. To
this aim, this paper considers a structured formalization of the problem which
is usually used for the analysis of text, and discusses it's suitability in the
context of Visual Sentiment Analysis. The paper also includes a description of
new challenges, the evaluation from the viewpoint of progress toward more
sophisticated systems and related practical applications, as well as a summary
of the insights resulting from this study.Comment: This paper is a postprint of a paper accepted by IET Image Processing
and is subject to Institution of Engineering and Technology Copyright. When
the final version is published, the copy of record will be available at the
IET Digital Librar