34,038 research outputs found
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
In this paper we evaluate performance of data-dependent hashing methods on
binary data. The goal is to find a hashing method that can effectively produce
lower dimensional binary representation of 512-bit FREAK descriptors. A
representative sample of recent unsupervised, semi-supervised and supervised
hashing methods was experimentally evaluated on large datasets of labelled
binary FREAK feature descriptors
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Deep Unsupervised Similarity Learning using Partially Ordered Sets
Unsupervised learning of visual similarities is of paramount importance to
computer vision, particularly due to lacking training data for fine-grained
similarities. Deep learning of similarities is often based on relationships
between pairs or triplets of samples. Many of these relations are unreliable
and mutually contradicting, implying inconsistencies when trained without
supervision information that relates different tuples or triplets to each
other. To overcome this problem, we use local estimates of reliable
(dis-)similarities to initially group samples into compact surrogate classes
and use local partial orders of samples to classes to link classes to each
other. Similarity learning is then formulated as a partial ordering task with
soft correspondences of all samples to classes. Adopting a strategy of
self-supervision, a CNN is trained to optimally represent samples in a mutually
consistent manner while updating the classes. The similarity learning and
grouping procedure are integrated in a single model and optimized jointly. The
proposed unsupervised approach shows competitive performance on detailed pose
estimation and object classification.Comment: Accepted for publication at IEEE Computer Vision and Pattern
Recognition 201
- …