2,409 research outputs found
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
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
SalAd: A Multimodal Approach for Contextual Video Advertising
The explosive growth of multimedia data on Internet has created huge opportunities for online video advertising. In this paper, we propose a novel advertising technique called SalAd, which utilizes textual information, visual content and the webpage saliency, to automatically associate the most suitable companion ads with online videos. Unlike most existing approaches that only focus on selecting the most relevant ads, SalAd further considers the saliency of selected ads to reduce intentional ignorance. SalAd consists of three basic steps. Given an online video and a set of advertisements, we first roughly identify a set of relevant ads based on the textual information matching. We then carefully select a sub-set of candidates based on visual content matching. In this regard, our selected ads are contextually relevant to online video content in terms of both textual information and visual content. We finally select the most salient ad among the relevant ads as the most appropriate one. To demonstrate the effectiveness of our method, we have conducted a rigorous eye-tracking experiment on two ad-datasets. The experimental results show that our method enhances the user engagement with the ad content while maintaining users\u27 quality of video viewing experience
Registration and categorization of camera captured documents
Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc.
However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT).
Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the Fâmeasure accuracy using the later approach as compared to the former
Trademarks and Textual Data: A Broader Perspective on Innovation = Marques et donnĂ©es textuelles : Une perspective Ă©largie sur lâinnovation
Patente messen hĂ€ufig technische Innovationen, wĂ€hrend Handelsmarken Low-Tech und Dienstleistungen abdecken. In dieser Arbeit werden Textdaten von Marken untersucht, um verschiedene Rechte des geistigen Eigentums zu kombinieren. Textdaten ermöglichen zum Beispiel die Analyse groĂer Datenmengen, die Kombination verschiedener Quellen und datengestĂŒtzte Erkenntnisse. Die Kombination von Handelsmarken und Patenten in den Bereichen Robotik (Hightech) und Schuhe (Lowtech) bietet eine breitere Abdeckung und Details zu Innovationen, die je nach Sektor variieren. Im Musikinstrumentensektor verdeutlichen Textdaten zu Marken, Patenten und Designs den laufenden technologischen Wandel. Patente beziehen sich auf Daten und Digitalisierungsthemen und werden von High-Tech-Firmen genutzt, wĂ€hrend Handelsmarken die Signalverarbeitung und Videospiele von Spielfirmen abdecken. Designs fungieren als verbindendes Element. Eine Differenzierung zwischen Unternehmen und TĂ€tigkeitsbereichen ist möglich. Zusammenfassend zeigt die These, dass die Integration von textuellen Markendaten die Innovationsabdeckung erweitert
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