1,429 research outputs found
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
Hyperspectral image (HSI) classification is widely used for the analysis of
remotely sensed images. Hyperspectral imagery includes varying bands of images.
Convolutional Neural Network (CNN) is one of the most frequently used deep
learning based methods for visual data processing. The use of CNN for HSI
classification is also visible in recent works. These approaches are mostly
based on 2D CNN. Whereas, the HSI classification performance is highly
dependent on both spatial and spectral information. Very few methods have
utilized the 3D CNN because of increased computational complexity. This letter
proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI
classification. Basically, the HybridSN is a spectral-spatial 3D-CNN followed
by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature
representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN
further learns more abstract level spatial representation. Moreover, the use of
hybrid CNNs reduces the complexity of the model compared to 3D-CNN alone. To
test the performance of this hybrid approach, very rigorous HSI classification
experiments are performed over Indian Pines, Pavia University and Salinas Scene
remote sensing datasets. The results are compared with the state-of-the-art
hand-crafted as well as end-to-end deep learning based methods. A very
satisfactory performance is obtained using the proposed HybridSN for HSI
classification. The source code can be found at
\url{https://github.com/gokriznastic/HybridSN}.Comment: Published in IEEE Geoscience and Remote Sensing Letter
Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search
Hash based nearest neighbor search has become attractive in many
applications. However, the quantization in hashing usually degenerates the
discriminative power when using Hamming distance ranking. Besides, for
large-scale visual search, existing hashing methods cannot directly support the
efficient search over the data with multiple sources, and while the literature
has shown that adaptively incorporating complementary information from diverse
sources or views can significantly boost the search performance. To address the
problems, this paper proposes a novel and generic approach to building multiple
hash tables with multiple views and generating fine-grained ranking results at
bitwise and tablewise levels. For each hash table, a query-adaptive bitwise
weighting is introduced to alleviate the quantization loss by simultaneously
exploiting the quality of hash functions and their complement for nearest
neighbor search. From the tablewise aspect, multiple hash tables are built for
different data views as a joint index, over which a query-specific rank fusion
is proposed to rerank all results from the bitwise ranking by diffusing in a
graph. Comprehensive experiments on image search over three well-known
benchmarks show that the proposed method achieves up to 17.11% and 20.28%
performance gains on single and multiple table search over state-of-the-art
methods
Learning to Hash for Indexing Big Data - A Survey
The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution using exhaustive comparison is infeasible due to the prohibitive
computational complexity and memory requirement. In response, Approximate
Nearest Neighbor (ANN) search based on hashing techniques has become popular
due to its promising performance in both efficiency and accuracy. Prior
randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore
data-independent hash functions with random projections or permutations.
Although having elegant theoretic guarantees on the search quality in certain
metric spaces, performance of randomized hashing has been shown insufficient in
many real-world applications. As a remedy, new approaches incorporating
data-driven learning methods in development of advanced hash functions have
emerged. Such learning to hash methods exploit information such as data
distributions or class labels when optimizing the hash codes or functions.
Importantly, the learned hash codes are able to preserve the proximity of
neighboring data in the original feature spaces in the hash code spaces. The
goal of this paper is to provide readers with systematic understanding of
insights, pros and cons of the emerging techniques. We provide a comprehensive
survey of the learning to hash framework and representative techniques of
various types, including unsupervised, semi-supervised, and supervised. In
addition, we also summarize recent hashing approaches utilizing the deep
learning models. Finally, we discuss the future direction and trends of
research in this area
Recent Advance in Content-based Image Retrieval: A Literature Survey
The explosive increase and ubiquitous accessibility of visual data on the Web
have led to the prosperity of research activity in image search or retrieval.
With the ignorance of visual content as a ranking clue, methods with text
search techniques for visual retrieval may suffer inconsistency between the
text words and visual content. Content-based image retrieval (CBIR), which
makes use of the representation of visual content to identify relevant images,
has attracted sustained attention in recent two decades. Such a problem is
challenging due to the intention gap and the semantic gap problems. Numerous
techniques have been developed for content-based image retrieval in the last
decade. The purpose of this paper is to categorize and evaluate those
algorithms proposed during the period of 2003 to 2016. We conclude with several
promising directions for future research.Comment: 22 page
Deep Learning at the Edge
The ever-increasing number of Internet of Things (IoT) devices has created a
new computing paradigm, called edge computing, where most of the computations
are performed at the edge devices, rather than on centralized servers. An edge
device is an electronic device that provides connections to service providers
and other edge devices; typically, such devices have limited resources. Since
edge devices are resource-constrained, the task of launching algorithms,
methods, and applications onto edge devices is considered to be a significant
challenge. In this paper, we discuss one of the most widely used machine
learning methods, namely, Deep Learning (DL) and offer a short survey on the
recent approaches used to map DL onto the edge computing paradigm. We also
provide relevant discussions about selected applications that would greatly
benefit from DL at the edge.Comment: 7 Pages, 79 References, CSCI201
Rank Subspace Learning for Compact Hash Codes
The era of Big Data has spawned unprecedented interests in developing hashing
algorithms for efficient storage and fast nearest neighbor search. Most
existing work learn hash functions that are numeric quantizations of feature
values in projected feature space. In this work, we propose a novel hash
learning framework that encodes feature's rank orders instead of numeric values
in a number of optimal low-dimensional ranking subspaces. We formulate the
ranking subspace learning problem as the optimization of a piece-wise linear
convex-concave function and present two versions of our algorithm: one with
independent optimization of each hash bit and the other exploiting a sequential
learning framework. Our work is a generalization of the Winner-Take-All (WTA)
hash family and naturally enjoys all the numeric stability benefits of rank
correlation measures while being optimized to achieve high precision at very
short code length. We compare with several state-of-the-art hashing algorithms
in both supervised and unsupervised domain, showing superior performance in a
number of data sets.Comment: 10 page
A Decade Survey of Content Based Image Retrieval using Deep Learning
The content based image retrieval aims to find the similar images from a
large scale dataset against a query image. Generally, the similarity between
the representative features of the query image and dataset images is used to
rank the images for retrieval. In early days, various hand designed feature
descriptors have been investigated based on the visual cues such as color,
texture, shape, etc. that represent the images. However, the deep learning has
emerged as a dominating alternative of hand-designed feature engineering from a
decade. It learns the features automatically from the data. This paper presents
a comprehensive survey of deep learning based developments in the past decade
for content based image retrieval. The categorization of existing
state-of-the-art methods from different perspectives is also performed for
greater understanding of the progress. The taxonomy used in this survey covers
different supervision, different networks, different descriptor type and
different retrieval type. A performance analysis is also performed using the
state-of-the-art methods. The insights are also presented for the benefit of
the researchers to observe the progress and to make the best choices. The
survey presented in this paper will help in further research progress in image
retrieval using deep learning
Malytics: A Malware Detection Scheme
An important problem of cyber-security is malware analysis. Besides good
precision and recognition rate, a malware detection scheme needs to be able to
generalize well for novel malware families (a.k.a zero-day attacks). It is
important that the system does not require excessive computation particularly
for deployment on the mobile devices. In this paper, we propose a novel scheme
to detect malware which we call Malytics. It is not dependent on any particular
tool or operating system. It extracts static features of any given binary file
to distinguish malware from benign. Malytics consists of three stages: feature
extraction, similarity measurement and classification. The three phases are
implemented by a neural network with two hidden layers and an output layer. We
show feature extraction, which is performed by tf -simhashing, is equivalent to
the first layer of a particular neural network. We evaluate Malytics
performance on both Android and Windows platforms. Malytics outperforms a wide
range of learning-based techniques and also individual state-of-the-art models
on both platforms. We also show Malytics is resilient and robust in addressing
zero-day malware samples. The F1-score of Malytics is 97.21% and 99.45% on
Android dex file and Windows PE files respectively, in the applied datasets.
The speed and efficiency of Malytics are also evaluated
Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images
Driven by the urgent demand for managing remote sensing big data, large-scale
remote sensing image retrieval (RSIR) attracts increasing attention in the
remote sensing field. In general, existing retrieval methods can be regarded as
visual-based retrieval approaches which search and return a set of similar
images from a database to a given query image. Although retrieval methods have
achieved great success, there is still a question that needs to be responded
to: Can we obtain the accurate semantic labels of the returned similar images
to further help analyzing and processing imagery? Inspired by the above
question, in this paper, we redefine the image retrieval problem as visual and
semantic retrieval of images. Specifically, we propose a novel deep hashing
convolutional neural network (DHCNN) to simultaneously retrieve the similar
images and classify their semantic labels in a unified framework. In more
detail, a convolutional neural network (CNN) is used to extract
high-dimensional deep features. Then, a hash layer is perfectly inserted into
the network to transfer the deep features into compact hash codes. In addition,
a fully connected layer with a softmax function is performed on hash layer to
generate class distribution. Finally, a loss function is elaborately designed
to simultaneously consider the label loss of each image and similarity loss of
pairs of images. Experimental results on two remote sensing datasets
demonstrate that the proposed method achieves the state-of-art retrieval and
classification performance
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