76,813 research outputs found
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
Face Recognition: A Novel Multi-Level Taxonomy based Survey
In a world where security issues have been gaining growing importance, face
recognition systems have attracted increasing attention in multiple application
areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face
recognition systems, face recognition taxonomies allow a deeper dissection and
comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the
organization and categorization of available and emerging face recognition
solutions; this taxonomy may also guide researchers in the development of more
efficient face recognition solutions. The proposed multi-level taxonomy
considers levels related to the face structure, feature support and feature
extraction approach. Following the proposed taxonomy, a comprehensive survey of
representative face recognition solutions is presented. The paper concludes
with a discussion on current algorithmic and application related challenges
which may define future research directions for face recognition.Comment: This paper is a preprint of a paper submitted to IET Biometrics. If
accepted, the copy of record will be available at the IET Digital Librar
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
Intelligent fashion outfit composition becomes more and more popular in these
years. Some deep learning based approaches reveal competitive composition
recently. However, the unexplainable characteristic makes such deep learning
based approach cannot meet the the designer, businesses and consumers' urge to
comprehend the importance of different attributes in an outfit composition. To
realize interpretable and customized fashion outfit compositions, we propose a
partitioned embedding network to learn interpretable representations from
clothing items. The overall network architecture consists of three components:
an auto-encoder module, a supervised attributes module and a multi-independent
module. The auto-encoder module serves to encode all useful information into
the embedding. In the supervised attributes module, multiple attributes labels
are adopted to ensure that different parts of the overall embedding correspond
to different attributes. In the multi-independent module, adversarial operation
are adopted to fulfill the mutually independent constraint. With the
interpretable and partitioned embedding, we then construct an outfit
composition graph and an attribute matching map. Given specified attributes
description, our model can recommend a ranked list of outfit composition with
interpretable matching scores. Extensive experiments demonstrate that 1) the
partitioned embedding have unmingled parts which corresponding to different
attributes and 2) outfits recommended by our model are more desirable in
comparison with the existing methods
A constrained clustering based approach for matching a collection of feature sets
In this paper, we consider the problem of finding the feature correspondences
among a collection of feature sets, by using their point-wise unary features.
This is a fundamental problem in computer vision and pattern recognition, which
also closely relates to other areas such as operational research. Different
from two-set matching which can be transformed to a quadratic assignment
programming task that is known NP-hard, inclusion of merely unary attributes
leads to a linear assignment problem for matching two feature sets. This
problem has been well studied and there are effective polynomial global optimum
solvers such as the Hungarian method. However, it becomes ill-posed when the
unary attributes are (heavily) corrupted. The global optimal correspondence
concerning the best score defined by the attribute affinity/cost between the
two sets can be distinct to the ground truth correspondence since the score
function is biased by noises. To combat this issue, we devise a method for
matching a collection of feature sets by synergetically exploring the
information across the sets. In general, our method can be perceived from a
(constrained) clustering perspective: in each iteration, it assigns the
features of one set to the clusters formed by the rest of feature sets, and
updates the cluster centers in turn. Results on both synthetic data and real
images suggest the efficacy of our method against state-of-the-arts.Comment: submission to ICPR 201
Properties of the Sample Mean in Graph Spaces and the Majorize-Minimize-Mean Algorithm
One of the most fundamental concepts in statistics is the concept of sample
mean. Properties of the sample mean that are well-defined in Euclidean spaces
become unwieldy or even unclear in graph spaces. Open problems related to the
sample mean of graphs include: non-existence, non-uniqueness, statistical
inconsistency, lack of convergence results of mean algorithms, non-existence of
midpoints, and disparity to midpoints. We present conditions to resolve all six
problems and propose a Majorize-Minimize-Mean (MMM) Algorithm. Experiments on
graph datasets representing images and molecules show that the MMM-Algorithm
best approximates a sample mean of graphs compared to six other mean
algorithms
Seeded Graph Matching
Given two graphs, the graph matching problem is to align the two vertex sets
so as to minimize the number of adjacency disagreements between the two graphs.
The seeded graph matching problem is the graph matching problem when we are
first given a partial alignment that we are tasked with completing. In this
paper, we modify the state-of-the-art approximate graph matching algorithm
"FAQ" of Vogelstein et al. (2015) to make it a fast approximate seeded graph
matching algorithm, adapt its applicability to include graphs with differently
sized vertex sets, and extend the algorithm so as to provide, for each
individual vertex, a nomination list of likely matches. We demonstrate the
effectiveness of our algorithm via simulation and real data experiments;
indeed, knowledge of even a few seeds can be extremely effective when our
seeded graph matching algorithm is used to recover a naturally existing
alignment that is only partially observed.Comment: 24 pages, 10 figure
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
This paper is focused on the task of searching for a specific vehicle that
appeared in the surveillance networks. Existing methods usually assume the
vehicle images are well cropped from the surveillance videos, then use visual
attributes, like colors and types, or license plate numbers to match the target
vehicle in the image set. However, a complete vehicle search system should
consider the problems of vehicle detection, representation, indexing, storage,
matching, and so on. Besides, attribute-based search cannot accurately find the
same vehicle due to intra-instance changes in different cameras and the
extremely uncertain environment. Moreover, the license plates may be
misrecognized in surveillance scenes due to the low resolution and noise. In
this paper, a Progressive Vehicle Search System, named as PVSS, is designed to
solve the above problems. PVSS is constituted of three modules: the crawler,
the indexer, and the searcher. The vehicle crawler aims to detect and track
vehicles in surveillance videos and transfer the captured vehicle images,
metadata and contextual information to the server or cloud. Then multi-grained
attributes, such as the visual features and license plate fingerprints, are
extracted and indexed by the vehicle indexer. At last, a query triplet with an
input vehicle image, the time range, and the spatial scope is taken as the
input by the vehicle searcher. The target vehicle will be searched in the
database by a progressive process. Extensive experiments on the public dataset
from a real surveillance network validate the effectiveness of the PVSS
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
Despite the fact that Second Order Similarity (SOS) has been used with
significant success in tasks such as graph matching and clustering, it has not
been exploited for learning local descriptors. In this work, we explore the
potential of SOS in the field of descriptor learning by building upon the
intuition that a positive pair of matching points should exhibit similar
distances with respect to other points in the embedding space. Thus, we propose
a novel regularization term, named Second Order Similarity Regularization
(SOSR), that follows this principle. By incorporating SOSR into training, our
learned descriptor achieves state-of-the-art performance on several challenging
benchmarks containing distinct tasks ranging from local patch retrieval to
structure from motion. Furthermore, by designing a von Mises-Fischer
distribution based evaluation method, we link the utilization of the descriptor
space to the matching performance, thus demonstrating the effectiveness of our
proposed SOSR. Extensive experimental results, empirical evidence, and in-depth
analysis are provided, indicating that SOSR can significantly boost the
matching performance of the learned descriptor
The Unconstrained Ear Recognition Challenge
In this paper we present the results of the Unconstrained Ear Recognition
Challenge (UERC), a group benchmarking effort centered around the problem of
person recognition from ear images captured in uncontrolled conditions. The
goal of the challenge was to assess the performance of existing ear recognition
techniques on a challenging large-scale dataset and identify open problems that
need to be addressed in the future. Five groups from three continents
participated in the challenge and contributed six ear recognition techniques
for the evaluation, while multiple baselines were made available for the
challenge by the UERC organizers. A comprehensive analysis was conducted with
all participating approaches addressing essential research questions pertaining
to the sensitivity of the technology to head rotation, flipping, gallery size,
large-scale recognition and others. The top performer of the UERC was found to
ensure robust performance on a smaller part of the dataset (with 180 subjects)
regardless of image characteristics, but still exhibited a significant
performance drop when the entire dataset comprising 3,704 subjects was used for
testing.Comment: International Joint Conference on Biometrics 201
Graph Kernels based on High Order Graphlet Parsing and Hashing
Graph-based methods are known to be successful in many machine learning and
pattern classification tasks. These methods consider semi-structured data as
graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives.
However, these non-vectorial graph data cannot be straightforwardly plugged
into off-the-shelf machine learning algorithms without a preliminary step of --
explicit/implicit -- graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly
discriminant. In this paper, we propose a novel high-order stochastic graphlet
embedding (SGE) that maps graphs into vector spaces. Our main contribution
includes a new stochastic search procedure that efficiently parses a given
graph and extracts/samples unlimitedly high-order graphlets. We consider these
graphlets, with increasing orders, to model local primitives as well as their
increasingly complex interactions. In order to build our graph representation,
we measure the distribution of these graphlets into a given graph, using
particular hash functions that efficiently assign sampled graphlets into
isomorphic sets with a very low probability of collision. When combined with
maximum margin classifiers, these graphlet-based representations have positive
impact on the performance of pattern comparison and recognition as corroborated
through extensive experiments using standard benchmark databases.Comment: arXiv admin note: substantial text overlap with arXiv:1702.0015
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