517 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades
The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models
Modeling small objects under uncertainties : novel algorithms and applications.
Active Shape Models (ASM), Active Appearance Models (AAM) and Active Tensor Models (ATM) are common approaches to model elastic (deformable) objects. These models require an ensemble of shapes and textures, annotated by human experts, in order identify the model order and parameters. A candidate object may be represented by a weighted sum of basis generated by an optimization process. These methods have been very effective for modeling deformable objects in biomedical imaging, biometrics, computer vision and graphics. They have been tried mainly on objects with known features that are amenable to manual (expert) annotation. They have not been examined on objects with severe ambiguities to be uniquely characterized by experts. This dissertation presents a unified approach for modeling, detecting, segmenting and categorizing small objects under uncertainty, with focus on lung nodules that may appear in low dose CT (LDCT) scans of the human chest. The AAM, ASM and the ATM approaches are used for the first time on this application. A new formulation to object detection by template matching, as an energy optimization, is introduced. Nine similarity measures of matching have been quantitatively evaluated for detecting nodules less than 1 em in diameter. Statistical methods that combine intensity, shape and spatial interaction are examined for segmentation of small size objects. Extensions of the intensity model using the linear combination of Gaussians (LCG) approach are introduced, in order to estimate the number of modes in the LCG equation. The classical maximum a posteriori (MAP) segmentation approach has been adapted to handle segmentation of small size lung nodules that are randomly located in the lung tissue. A novel empirical approach has been devised to simultaneously detect and segment the lung nodules in LDCT scans. The level sets methods approach was also applied for lung nodule segmentation. A new formulation for the energy function controlling the level set propagation has been introduced taking into account the specific properties of the nodules. Finally, a novel approach for classification of the segmented nodules into categories has been introduced. Geometric object descriptors such as the SIFT, AS 1FT, SURF and LBP have been used for feature extraction and matching of small size lung nodules; the LBP has been found to be the most robust. Categorization implies classification of detected and segmented objects into classes or types. The object descriptors have been deployed in the detection step for false positive reduction, and in the categorization stage to assign a class and type for the nodules. The AAMI ASMI A TM models have been used for the categorization stage. The front-end processes of lung nodule modeling, detection, segmentation and classification/categorization are model-based and data-driven. This dissertation is the first attempt in the literature at creating an entirely model-based approach for lung nodule analysis
Automated Diatom Classification (Part A): Handcrafted Feature Approaches
This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images[EN] This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatomâs valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future workSIThe authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO), http://aqualitas-retos.es/en
SigSpace â Class-Based Feature Representation for Scalable and Distributed Machine Learning
Title from PDF of title page, viewed on October 25, 2016Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 70-71)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016In the era of big data, it is essential to explore the opportunities in discovering knowledge
from big data. However, traditional machine learning approaches are not well fit
to analyze the full value of big data. Explicitly, current research and practice of Machine
learning do not fully support some important features for big data analytics such as
incremental learning, distributed learning, and fuzzy matching.
In this thesis, we propose a unique feature representation, named the SigSpace.
It is designed for a class-level incremental learning in support to distributed learning and
fuzzy matching. In SigSpace, a class-based model was built by an evaluation and extension
of existing machine learning models, i.e., K-means and Self-Organizing Maps
(SOM). The Machine learning with SigSpace is modeled as a feature set with standard
machine learning algorithms like Random Forests, Decision Tree etc., and a class model
using L1 (Manhattan distance) and L2 (Euclidean distance) norms.
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In order to provide supporting evidence for the effectiveness of SigSpace, we have
conducted comprehensive experiments as follows: Firstly, multiple experiments were
conducted to evaluate the SigSpace model in image classification using large scale image
datasets including Caltech-101, Caltech-256, ImageNet, UEC FOOD 256, MNIST
with image features like Pixels, SIFT, and Local Binary Pattern. Secondly, SigSpace was
evaluated in the audio classification context with imperative audio features extracted from
real-time audio datasets. The SigSpace system was implanted using a Big data analytics tool, Apache
Spark(MLLib) with the capability of parallel and distributed learning and recognition.
The experiments of multinomial classification were conducted with 6 to 1000 classes,
space requirements in megabytes to terabytes, and learning time ranging from minutes to
days. Although there has been a slight accuracy decrease (approximately 5%) in the overall
performance, SigSpace is very efficient, in terms of space as well as runtime performance
for learning and recognition. Thus, the current evaluation confirms that SigSpace
has a significant approach for distributed and scalable Machine learning with big data.Introduction -- Background and related work -- Proposed solution: SigSpace -- Implementation and evaluation -- Conclusion and future wor
Trademark image retrieval by local features
The challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current
operational trademark retrieval systems involve manual annotation of the images
(the current âgold standardâ). Accordingly, current systems require a substantial
amount of time and labour to access, and are therefore expensive to operate. This
thesis focuses on the development of algorithms that mimic aspects of human
visual perception in order to retrieve similar abstract trademark images
automatically. A significant category of trademark images are typically highly
stylised, comprising a collection of distinctive graphical elements that often
include geometric shapes. Therefore, in order to compare the similarity of such
images the principal aim of this research has been to develop a method for solving
the partial matching and shape perception problem.
There are few useful techniques for partial shape matching in the context of
trademark retrieval, because those existing techniques tend not to support multicomponent
retrieval. When this work was initiated most trademark image
retrieval systems represented images by means of global features, which are not
suited to solving the partial matching problem. Instead, the author has
investigated the use of local image features as a means to finding similarities
between trademark images that only partially match in terms of their subcomponents.
During the course of this work, it has been established that the
Harris and Chabat detectors could potentially perform sufficiently well to serve as
the basis for local feature extraction in trademark image retrieval. Early findings
in this investigation indicated that the well established SIFT (Scale Invariant
Feature Transform) local features, based on the Harris detector, could potentially
serve as an adequate underlying local representation for matching trademark
images.
There are few researchers who have used mechanisms based on human
perception for trademark image retrieval, implying that the shape representations
utilised in the past to solve this problem do not necessarily reflect the shapes
contained in these image, as characterised by human perception. In response, a
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practical approach to trademark image retrieval by perceptual grouping has been
developed based on defining meta-features that are calculated from the spatial
configurations of SIFT local image features. This new technique measures certain
visual properties of the appearance of images containing multiple graphical
elements and supports perceptual grouping by exploiting the non-accidental
properties of their configuration.
Our validation experiments indicated that we were indeed able to capture
and quantify the differences in the global arrangement of sub-components evident
when comparing stylised images in terms of their visual appearance properties.
Such visual appearance properties, measured using 17 of the proposed metafeatures,
include relative sub-component proximity, similarity, rotation and
symmetry. Similar work on meta-features, based on the above Gestalt proximity,
similarity, and simplicity groupings of local features, had not been reported in the
current computer vision literature at the time of undertaking this work.
We decided to adopted relevance feedback to allow the visual appearance
properties of relevant and non-relevant images returned in response to a query to
be determined by example. Since limited training data is available when
constructing a relevance classifier by means of user supplied relevance feedback,
the intrinsically non-parametric machine learning algorithm ID3 (Iterative
Dichotomiser 3) was selected to construct decision trees by means of dynamic
rule induction. We believe that the above approach to capturing high-level visual
concepts, encoded by means of meta-features specified by example through
relevance feedback and decision tree classification, to support flexible trademark
image retrieval and to be wholly novel.
The retrieval performance the above system was compared with two other
state-of-the-art image trademark retrieval systems: Artisan developed by Eakins
(Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using
relevance feedback, our system achieves higher average normalised precision
than either of the systems developed by Eakinsâ or Jiang. However, while our
trademark image query and database set is based on an image dataset used by
Eakins, we employed different numbers of images. It was not possible to access to
the same query set and image database used in the evaluation of Jiangâs trademark
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image retrieval system evaluation. Despite these differences in evaluation
methodology, our approach would appear to have the potential to improve
retrieval effectiveness
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