61,271 research outputs found
Deep Metric Multi-View Hashing for Multimedia Retrieval
Learning the hash representation of multi-view heterogeneous data is an
important task in multimedia retrieval. However, existing methods fail to
effectively fuse the multi-view features and utilize the metric information
provided by the dissimilar samples, leading to limited retrieval precision.
Current methods utilize weighted sum or concatenation to fuse the multi-view
features. We argue that these fusion methods cannot capture the interaction
among different views. Furthermore, these methods ignored the information
provided by the dissimilar samples. We propose a novel deep metric multi-view
hashing (DMMVH) method to address the mentioned problems. Extensive empirical
evidence is presented to show that gate-based fusion is better than typical
methods. We introduce deep metric learning to the multi-view hashing problems,
which can utilize metric information of dissimilar samples. On the
MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current
state-of-the-art methods by a large margin (up to 15.28 mean Average Precision
(mAP) improvement).Comment: Accepted by IEEE ICME 202
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
- …