33 research outputs found
ECCH: A novel color coocurrence histogram
In this paper, a novel color cooccurrence histogram method, named eCCH which stands for color cooccurrence histogram at edge points, is proposed to describe the spatial-color joint distribution of images. Unlike all existing ideas, we only investigate the color distribution of pixels located at the two sides of edge points on gradient direction lines. When measuring the similarity of two eCCHs, the Gaussian weighted histogram intersection method is adopted, where both identical and similar color pairs are considered to compensate color variations. Comparative experimental results demonstrate the performance of the proposed eCCH in terms of robustness to color variance and small computational complexity. ©2010 IEEE
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
Large Scale Pattern Detection in Videos and Images from the Wild
PhDPattern detection is a well-studied area of computer vision, but still current methods are
unstable in images of poor quality. This thesis describes improvements over contemporary
methods in the fast detection of unseen patterns in a large corpus of videos that vary
tremendously in colour and texture definition, captured âin the wildâ by mobile devices
and surveillance cameras.
We focus on three key areas of this broad subject;
First, we identify consistency weaknesses in existing techniques of processing an image
and itâs horizontally reflected (mirror) image. This is important in police investigations
where subjects change their appearance to try to avoid recognition, and we propose that
invariance to horizontal reflection should be more widely considered in image description
and recognition tasks too. We observe online Deep Learning system behaviours in
this respect, and provide a comprehensive assessment of 10 popular low level feature
detectors.
Second, we develop simple and fast algorithms that combine to provide memory- and
processing-efficient feature matching. These involve static scene elimination in the presence
of noise and on-screen time indicators, a blur-sensitive feature detection that finds
a greater number of corresponding features in images of varying sharpness, and a combinatorial
texture and colour feature matching algorithm that matches features when
either attribute may be poorly defined. A comprehensive evaluation is given, showing
some improvements over existing feature correspondence methods.
Finally, we study random decision forests for pattern detection. A new method of
indexing patterns in video sequences is devised and evaluated. We automatically label
positive and negative image training data, reducing a task of unsupervised learning to
one of supervised learning, and devise a node split function that is invariant to mirror
reflection and rotation through 90 degree angles. A high dimensional vote accumulator
encodes the hypothesis support, yielding implicit back-projection for pattern detection.European Unionâs Seventh Framework Programme, specific
topic âframework and tools for (semi-) automated exploitation of massive amounts of digital data
for forensic purposesâ, under grant agreement number 607480 (LASIE IP project)
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Text Segmentation in Web Images Using Colour Perception and Topological Features
The research presented in this thesis addresses the problem of Text Segmentation in Web images. Text is routinely created in image form (headers, banners etc.) on Web pages, as an attempt to overcome the stylistic limitations of HTML. This text however, has a potentially high semantic value in terms of indexing and searching for the corresponding Web pages. As current search engine technology does not allow for text extraction and recognition in images, the text in image form is ignored. Moreover, it is desirable to obtain a uniform representation of all visible text of a Web page (for applications such as voice browsing or automated content analysis). This thesis presents two methods for text segmentation in Web images using colour perception and topological features. The nature of Web images and the implicit problems to text segmentation are described, and a study is performed to assess the magnitude of the problem and establish the need for automated text segmentation methods. Two segmentation methods are subsequently presented: the Split-and-Merge segmentation method and the Fuzzy segmentation method. Although approached in a distinctly different way in each method, the safe assumption that a human being should be able to read the text in any given Web Image is the foundation of both methodsâ reasoning. This anthropocentric character of the methods along with the use of topological features of connected components, comprise the underlying working principles of the methods. An approach for classifying the connected components resulting from the segmentation methods as either characters or parts of the background is also presented
NASA Tech Briefs, May 1994
Topics covered include: Robotics/Automation; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports
Colour and Colorimetry Multidisciplinary Contributions Vol. XIb
It is well known that the subject of colour has an impact on a range of disciplines. Colour has been studied in depth for many centuries, and as well as contributing to theoretical and scientific knowledge, there have been significant developments in applied colour research, which has many implications for the wider socio-economic community. At the 7th Convention of Colorimetry in Parma, on the 1st October 2004, as an evolution of the previous SIOF Group of Colorimetry and Reflectoscopy founded in 1995, the "Gruppo del Colore" was established. The objective was to encourage multi and interdisciplinary collaboration and networking between people in Italy that addresses problems and issues on colour and illumination from a professional, cultural and scientific point of view. On the 16th of September 2011 in Rome, in occasion of the VII Color Conference, the members assembly decided to vote for the autonomy of the group. The autonomy of the Association has been achieved in early 2012. These are the proceedings of the English sessions of the XI Conferenza del Colore