13,758 research outputs found
Performance comparison of point and spatial access methods
In the past few years a large number of multidimensional point access methods, also called
multiattribute index structures, has been suggested, all of them claiming good performance. Since no
performance comparison of these structures under arbitrary (strongly correlated nonuniform, short
"ugly") data distributions and under various types of queries has been performed, database
researchers and designers were hesitant to use any of these new point access methods. As shown in
a recent paper, such point access methods are not only important in traditional database applications.
In new applications such as CAD/CIM and geographic or environmental information systems, access
methods for spatial objects are needed. As recently shown such access methods are based on point
access methods in terms of functionality and performance. Our performance comparison naturally
consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in
part I I spatial access methods for rectangles will be compared. In part I we present a survey and
classification of existing point access methods. Then we carefully select the following four methods
for implementation and performance comparison under seven different data files (distributions) and
various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called
the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the
BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is
robust under ugly data and queries. In part I I we compare spatial access methods for rectangles.
After presenting a survey and classification of existing spatial access methods we carefully selected
the following four methods for implementation and performance comparison under six different data
files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the
BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree.
This comparison is a first step towards a standardized testbed or benchmark. We offer our data and
query files to each designer of a new point or spatial access method such that he can run his
implementation in our testbed
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
Rapid Online Analysis of Local Feature Detectors and Their Complementarity
A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications. © 2013 by the authors; licensee MDPI, Basel, Switzerland
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
Locality of Corner Transformation for Multidimensional Spatial Access Methods
AbstractThe geometric structural complexity of spatial objects does not render an intuitive distance metric on the data space that measures spatial proximity. However, such a metric provides a formal basis for analytical work in transformation-based multidimensional spatial access methods, including locality preservation of the underlying transformation and distance-based spatial queries. We study the Hausdorff distance metric on the space of multidimensional polytopes, and prove a tight relationship between the metric on the original space of k-dimensional hyperrectangles and the standard p-normed metric on the transform space of 2k-dimensional points under the corner transformation, which justifies the effectiveness of the transformation-based technique in preserving spatial locality
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Spatially Ordered Treemaps
Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described
Onion Curve: A Space Filling Curve with Near-Optimal Clustering
Space filling curves (SFCs) are widely used in the design of indexes for
spatial and temporal data. Clustering is a key metric for an SFC, that measures
how well the curve preserves locality in moving from higher dimensions to a
single dimension. We present the {\em onion curve}, an SFC whose clustering
performance is provably close to optimal for the cube and near-cube shaped
query sets, irrespective of the side length of the query. We show that in
contrast, the clustering performance of the widely used Hilbert curve can be
far from optimal, even for cube-shaped queries. Since the clustering
performance of an SFC is critical to the efficiency of multi-dimensional
indexes based on the SFC, the onion curve can deliver improved performance for
data structures involving multi-dimensional data.Comment: The short version is published in ICDE 1
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