308 research outputs found
On Modeling the Behavior of Comparators for Complex Fuzzy Objects in a Fuzzy Object-Relational Database Management System
This paper proposes a parameterized definition for fuzzy comparators on complex fuzzy datatypes like
fuzzy collections with conjunctive semantics and fuzzy objects. This definition and its implementation
on a Fuzzy Object-Relational Database Management System (FORDBMS) provides the designer with a
powerful tool to adapt the behavior of these operators to the semantics of the considered application."Consejeria de Innovacion Ciencia y Empresa de Andalucia" (Spain) P06-TIC-01570
P07-TIC-02611Ministry of Science and Innovation, Spain (MICINN)
Spanish Government TIN2009-08296
TIN2007-68084-C02-0
Perception-based fuzzy partitions for visual texture modelling
Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like âvery coarseâ, âlow directionalâ, or âhigh contrastedâ. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure
Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems
YesThe exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.Supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Knowledge assisted data management and retrieval in multimedia database sistems
With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support usersâ interaction and to effectively model usersâ perception from the feedback at both the image-level and object-level
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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