3,639 research outputs found
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Image Understanding by Hierarchical Symbolic Representation and Inexact Matching of Attributed Graphs
We study the symbolic representation of imagery information by a powerful global representation scheme in the form of Attributed Relational Graph (ARG), and propose new techniques for the extraction of such representation from spatial-domain images, and for performing the task of image understanding through the analysis of the extracted ARG representation. To achieve practical image understanding tasks, the system needs to comprehend the imagery information in a global form. Therefore, we propose a multi-layer hierarchical scheme for the extraction of global symbolic representation from spatial-domain images. The proposed scheme produces a symbolic mapping of the input data in terms of an output alphabet, whose elements are defined over global subimages. The proposed scheme uses a combination of model-driven and data-driven concepts. The model- driven principle is represented by a graph transducer, which is used to specify the alphabet at each layer in the scheme. A symbolic mapping is driven by the input data to map the input local alphabet into the output global alphabet. Through the iterative application of the symbolic transformational mapping at different levels of hierarchy, the system extracts a global representation from the image in the form of attributed relational graphs. Further processing and interpretation of the imagery information can, then, be performed on their ARG representation. We also propose an efficient approach for calculating a distance measure and finding the best inexact matching configuration between attributed relational graphs. For two ARGs, we define sequences of weighted error-transformations which when performed on one ARG (or a subgraph of it), will produce the other ARG. A distance measure between two ARGs is defined as the weight of the sequence which possesses minimum total-weight. Moreover, this minimum-total weight sequence defines the best inexact matching configuration between the two ARGs. The global minimization over the possible sequences is performed by a dynamic programming technique, the approach shows good results for ARGs of practical sizes. The proposed system possesses the capability to inference the alphabets of the ARG representation which it uses. In the inference phase, the hierarchical scheme is usually driven by the input data only, which normally consist of images of model objects. It extracts the global alphabet of the ARG representation of the models. The extracted model representation is then used in the operation phase of the system to: perform the mapping in the multi-layer scheme. We present our experimental results for utilizing the proposed system for locating objects in complex scenes
Auto-completion of contours in sketches, maps and sparse 2D images based on topological persistence.
We design a new fast algorithm to automatically complete closed contours in a finite point cloud on the plane. The only input can be a scanned map with almost closed curves, a hand-drawn artistic sketch or any sparse dotted image in 2D without any extra parameters. The output is a hierarchy of closed contours that have a long enough life span (persistence) in a sequence of nested neighborhoods of the input points. We prove theoretical guarantees when, for a given noisy sample of a graph in the plane, the output contours geometrically approximate the original contours in the unknown graph
Shape-based invariant features extraction for object recognition
International audienceThe emergence of new technologies enables generating large quantity of digital information including images; this leads to an increasing number of generated digital images. Therefore it appears a necessity for automatic systems for image retrieval. These systems consist of techniques used for query specification and re-trieval of images from an image collection. The most frequent and the most com-mon means for image retrieval is the indexing using textual keywords. But for some special application domains and face to the huge quantity of images, key-words are no more sufficient or unpractical. Moreover, images are rich in content; so in order to overcome these mentioned difficulties, some approaches are pro-posed based on visual features derived directly from the content of the image: these are the content-based image retrieval (CBIR) approaches. They allow users to search the desired image by specifying image queries: a query can be an exam-ple, a sketch or visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring simi-larity between image features. An important property of these features is to be in-variant under various deformations that the observed image could undergo. In this chapter, we will present a number of existing methods for CBIR applica-tions. We will also describe some measures that are usually used for similarity measurement. At the end, and as an application example, we present a specific ap-proach, that we are developing, to illustrate the topic by providing experimental results
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm
Partial shape matching using CCP map and weighted graph transformation matching
La dĂ©tection de la similaritĂ© ou de la diffĂ©rence entre les images et leur mise en correspondance sont des problĂšmes fondamentaux dans le traitement de l'image. Pour rĂ©soudre ces problĂšmes, on utilise, dans la littĂ©rature, diffĂ©rents algorithmes d'appariement. MalgrĂ© leur nouveautĂ©, ces algorithmes sont pour la plupart inefficaces et ne peuvent pas fonctionner correctement dans les situations dâimages bruitĂ©es. Dans ce mĂ©moire, nous rĂ©solvons la plupart des problĂšmes de ces mĂ©thodes en utilisant un algorithme fiable pour segmenter la carte des contours image, appelĂ©e carte des CCPs, et une nouvelle mĂ©thode d'appariement. Dans notre algorithme, nous utilisons un descripteur local qui est rapide Ă calculer, est invariant aux transformations affines et est fiable pour des objets non rigides et des situations dâoccultation. AprĂšs avoir trouvĂ© le meilleur appariement pour chaque contour, nous devons vĂ©rifier si ces derniers sont correctement appariĂ©s. Pour ce faire, nous utilisons l'approche « Weighted Graph Transformation Matching » (WGTM), qui est capable d'Ă©liminer les appariements aberrants en fonction de leur proximitĂ© et de leurs relations gĂ©omĂ©triques. WGTM fonctionne correctement pour les objets Ă la fois rigides et non rigides et est robuste aux distorsions importantes. Pour Ă©valuer notre mĂ©thode, le jeu de donnĂ©es ETHZ comportant cinq classes diffĂ©rentes d'objets (bouteilles, cygnes, tasses, girafes, logos Apple) est utilisĂ©. Enfin, notre mĂ©thode est comparĂ©e Ă plusieurs mĂ©thodes cĂ©lĂšbres proposĂ©es par d'autres chercheurs dans la littĂ©rature. Bien que notre mĂ©thode donne un rĂ©sultat comparable Ă celui des mĂ©thodes de rĂ©fĂ©rence en termes du rappel et de la prĂ©cision de localisation des frontiĂšres, elle amĂ©liore significativement la prĂ©cision moyenne pour toutes les catĂ©gories du jeu de donnĂ©es ETHZ.Matching and detecting similarity or dissimilarity between images is a fundamental problem in image processing. Different matching algorithms are used in literature to solve this fundamental problem. Despite their novelty, these algorithms are mostly inefficient and cannot perform properly in noisy situations. In this thesis, we solve most of the problems of previous methods by using a reliable algorithm for segmenting image contour map, called CCP Map, and a new matching method. In our algorithm, we use a local shape descriptor that is very fast, invariant to affine transform, and robust for dealing with non-rigid objects and occlusion. After finding the best match for the contours, we need to verify if they are correctly matched. For this matter, we use the Weighted Graph Transformation Matching (WGTM) approach, which is capable of removing outliers based on their adjacency and geometrical relationships. WGTM works properly for both rigid and non-rigid objects and is robust to high order distortions. For evaluating our method, the ETHZ dataset including five diverse classes of objects (bottles, swans, mugs, giraffes, apple-logos) is used. Finally, our method is compared to several famous methods proposed by other researchers in the literature. While our method shows a comparable result to other benchmarks in terms of recall and the precision of boundary localization, it significantly improves the average precision for all of the categories in the ETHZ dataset
The QCD Coupling Constant
This paper presents a summary of the current status of determinations of the
strong coupling constant alpha_s. A detailed description of the definition,
scale dependence and inherent theoretical ambiguities is given. The various
physical processes that can be used to determine alpha_s are reviewed and
attention is given to the uncertainties, both theoretical and experimental.Comment: 56 page
The birth of the strong components
Random directed graphs undergo a phase transition around the point
, and the width of the transition window has been known since the
works of Luczak and Seierstad. They have established that as
when , the asymptotic probability that the strongly
connected components of a random directed graph are only cycles and single
vertices decreases from 1 to 0 as goes from to .
By using techniques from analytic combinatorics, we establish the exact
limiting value of this probability as a function of and provide more
properties of the structure of a random digraph around, below and above its
transition point. We obtain the limiting probability that a random digraph is
acyclic and the probability that it has one strongly connected complex
component with a given difference between the number of edges and vertices
(called excess). Our result can be extended to the case of several complex
components with given excesses as well in the whole range of sparse digraphs.
Our study is based on a general symbolic method which can deal with a great
variety of possible digraph families, and a version of the saddle-point method
which can be systematically applied to the complex contour integrals appearing
from the symbolic method. While the technically easiest model is the model of
random multidigraphs, in which multiple edges are allowed, and where edge
multiplicities are sampled independently according to a Poisson distribution
with a fixed parameter , we also show how to systematically approach the
family of simple digraphs, where multiple edges are forbidden, and where
2-cycles are either allowed or not.
Our theoretical predictions are supported by numerical simulations, and we
provide tables of numerical values for the integrals of Airy functions that
appear in this study.Comment: 62 pages, 12 figures, 6 tables. Supplementary computer algebra
computations available at https://gitlab.com/vit.north/strong-components-au
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