1,403 research outputs found
Selective Deep Convolutional Features for Image Retrieval
Convolutional Neural Network (CNN) is a very powerful approach to extract
discriminative local descriptors for effective image search. Recent work adopts
fine-tuned strategies to further improve the discriminative power of the
descriptors. Taking a different approach, in this paper, we propose a novel
framework to achieve competitive retrieval performance. Firstly, we propose
various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a
representative subset of local convolutional features and remove a large number
of redundant features. We demonstrate that this can effectively address the
burstiness issue and improve retrieval accuracy. Secondly, we propose to employ
recent embedding and aggregating methods to further enhance feature
discriminability. Extensive experiments demonstrate that our proposed framework
achieves state-of-the-art retrieval accuracy.Comment: Accepted to ACM MM 201
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
State-of-the-art person re-identification systems that employ a triplet based
deep network suffer from a poor generalization capability. In this paper, we
propose a four stream Siamese deep convolutional neural network for person
redetection that jointly optimises verification and identification losses over
a four image input group. Specifically, the proposed method overcomes the
weakness of the typical triplet formulation by using groups of four images
featuring two matched (i.e. the same identity) and two mismatched images. This
allows us to jointly increase the interclass variations and reduce the
intra-class variations in the learned feature space. The proposed approach also
optimises over both the identification and verification losses, further
minimising intra-class variation and maximising inter-class variation,
improving overall performance. Extensive experiments on four challenging
datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed
approach achieves state-of-the-art performance.Comment: Published in WACV 201
Deep Image Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared
from various fields, such as social media platforms, medical images, and
robotics. This abundance of content creation and sharing has introduced new
challenges. In particular, searching databases for similar content, i.e.content
based image retrieval (CBIR), is a long-established research area, and more
efficient and accurate methods are needed for real time retrieval. Artificial
intelligence has made progress in CBIR and has significantly facilitated the
process of intelligent search. In this survey we organize and review recent
CBIR works that are developed based on deep learning algorithms and techniques,
including insights and techniques from recent papers. We identify and present
the commonly-used benchmarks and evaluation methods used in the field. We
collect common challenges and propose promising future directions. More
specifically, we focus on image retrieval with deep learning and organize the
state of the art methods according to the types of deep network structure, deep
features, feature enhancement methods, and network fine-tuning strategies. Our
survey considers a wide variety of recent methods, aiming to promote a global
view of the field of instance-based CBIR.Comment: 20 pages, 11 figure
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the
most-used public dataset for evaluation in machine listening research for music
genre recognition (MGR). Our recent work, however, shows GTZAN has several
faults (repetitions, mislabelings, and distortions), which challenge the
interpretability of any result derived using it. In this article, we disprove
the claims that all MGR systems are affected in the same ways by these faults,
and that the performances of MGR systems in GTZAN are still meaningfully
comparable since they all face the same faults. We identify and analyze the
contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has
been used in MGR research, and find few indications that its faults have been
known and considered. Finally, we rigorously study the effects of its faults on
evaluating five different MGR systems. The lesson is not to banish GTZAN, but
to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference
Integration of informal music technologies in secondary school music lessons
date-added: 2011-08-12 11:03:06 +0100 date-modified: 2011-08-12 11:03:38 +0100date-added: 2011-08-12 11:03:06 +0100 date-modified: 2011-08-12 11:03:38 +0100This project was supported by EPSRC grant EP/I001832/1, ‘Musicology for the masses’
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval
There are great demands for automatically regulating inappropriate appearance
of shocking firearm images in social media or identifying firearm types in
forensics. Image retrieval techniques have great potential to solve these
problems. To facilitate research in this area, we introduce Firearm 14k, a
large dataset consisting of over 14,000 images in 167 categories. It can be
used for both fine-grained recognition and retrieval of firearm images. Recent
advances in image retrieval are mainly driven by fine-tuning state-of-the-art
convolutional neural networks for retrieval task. The conventional single
margin contrastive loss, known for its simplicity and good performance, has
been widely used. We find that it performs poorly on the Firearm 14k dataset
due to: (1) Loss contributed by positive and negative image pairs is unbalanced
during training process. (2) A huge domain gap exists between this dataset and
ImageNet. We propose to deal with the unbalanced loss by employing a double
margin contrastive loss. We tackle the domain gap issue with a two-stage
training strategy, where we first fine-tune the network for classification, and
then fine-tune it for retrieval. Experimental results show that our approach
outperforms the conventional single margin approach by a large margin (up to
88.5% relative improvement) and even surpasses the strong triplet-loss-based
approach.Comment: 6 pages, 5 figures, accepted by ICPR 2018. Code are available at
https://github.com/jdhao/deep_firearm. Dataset is available at
http://forensics.idealtest.org/Firearm14k
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