431 research outputs found
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
Automatic Summarization of Soccer Highlights Using Audio-visual Descriptors
Automatic summarization generation of sports video content has been object of
great interest for many years. Although semantic descriptions techniques have
been proposed, many of the approaches still rely on low-level video descriptors
that render quite limited results due to the complexity of the problem and to
the low capability of the descriptors to represent semantic content. In this
paper, a new approach for automatic highlights summarization generation of
soccer videos using audio-visual descriptors is presented. The approach is
based on the segmentation of the video sequence into shots that will be further
analyzed to determine its relevance and interest. Of special interest in the
approach is the use of the audio information that provides additional
robustness to the overall performance of the summarization system. For every
video shot a set of low and mid level audio-visual descriptors are computed and
lately adequately combined in order to obtain different relevance measures
based on empirical knowledge rules. The final summary is generated by selecting
those shots with highest interest according to the specifications of the user
and the results of relevance measures. A variety of results are presented with
real soccer video sequences that prove the validity of the approach
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
A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval
We introduce a shape descriptor that extracts keypoints from binary images and
automatically detects the salient ones among them. The proposed descriptor operates as
follows: First, the contours of the image are detected and an image transformation is used to
generate background information. Next, pixels of the transformed image that have specific
characteristics in their local areas are used to extract keypoints. Afterwards, the most salient
keypoints are automatically detected by filtering out redundant and sensitive ones. Finally,
a feature vector is calculated for each keypoint by using the distribution of contour points
in its local area. The proposed descriptor is evaluated using public datasets of silhouette
images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned
logos. Experimental results show that the proposed descriptor compares strongly against
state of the art methods, and that it is reliable when applied on challenging images such as
fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto
Participation of INRIA & Pl@ntNet to ImageCLEF 2011 plant images classification task
International audienceThis paper presents the participation of INRIA IMEDIA group and the Pl@ntNet project to ImageCLEF 2011 plant identification task. ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. IMEDIA submitted two runs to this task and obtained the best evaluation score for two of the three image categories addressed within the benchmark. The paper presents the two approaches employed, and provides an analysis of the obtained evaluation results
Shape-based image retrieval in iconic image databases.
by Chan Yuk Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 117-124).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.3Chapter 1.2 --- Designing a Shape-based Image Retrieval System --- p.4Chapter 1.3 --- Information on Trademark --- p.6Chapter 1.3.1 --- What is a Trademark? --- p.6Chapter 1.3.2 --- Search for Conflicting Trademarks --- p.7Chapter 1.3.3 --- Research Scope --- p.8Chapter 1.4 --- Information on Chinese Cursive Script Character --- p.9Chapter 1.5 --- Problem Definition --- p.9Chapter 1.6 --- Contributions --- p.11Chapter 1.7 --- Thesis Organization --- p.13Chapter 2 --- Literature Review --- p.14Chapter 2.1 --- Trademark Retrieval using QBIC Technology --- p.14Chapter 2.2 --- STAR --- p.16Chapter 2.3 --- ARTISAN --- p.17Chapter 2.4 --- Trademark Retrieval using a Visually Salient Feature --- p.18Chapter 2.5 --- Trademark Recognition using Closed Contours --- p.19Chapter 2.6 --- Trademark Retrieval using a Two Stage Hierarchy --- p.19Chapter 2.7 --- Logo Matching using Negative Shape Features --- p.21Chapter 2.8 --- Chapter Summary --- p.22Chapter 3 --- Background on Shape Representation and Matching --- p.24Chapter 3.1 --- Simple Geometric Features --- p.25Chapter 3.1.1 --- Circularity --- p.25Chapter 3.1.2 --- Rectangularity --- p.26Chapter 3.1.3 --- Hole Area Ratio --- p.27Chapter 3.1.4 --- Horizontal Gap Ratio --- p.27Chapter 3.1.5 --- Vertical Gap Ratio --- p.28Chapter 3.1.6 --- Central Moments --- p.28Chapter 3.1.7 --- Major Axis Orientation --- p.29Chapter 3.1.8 --- Eccentricity --- p.30Chapter 3.2 --- Fourier Descriptors --- p.30Chapter 3.3 --- Chain Codes --- p.31Chapter 3.4 --- Seven Invariant Moments --- p.33Chapter 3.5 --- Zernike Moments --- p.35Chapter 3.6 --- Edge Direction Histogram --- p.36Chapter 3.7 --- Curvature Scale Space Representation --- p.37Chapter 3.8 --- Chapter Summary --- p.39Chapter 4 --- Genetic Algorithm for Weight Assignment --- p.42Chapter 4.1 --- Genetic Algorithm (GA) --- p.42Chapter 4.1.1 --- Basic Idea --- p.43Chapter 4.1.2 --- Genetic Operators --- p.44Chapter 4.2 --- Why GA? --- p.45Chapter 4.3 --- Weight Assignment Problem --- p.46Chapter 4.3.1 --- Integration of Image Attributes --- p.46Chapter 4.4 --- Proposed Solution --- p.47Chapter 4.4.1 --- Formalization --- p.47Chapter 4.4.2 --- Proposed Genetic Algorithm --- p.43Chapter 4.5 --- Chapter Summary --- p.49Chapter 5 --- Shape-based Trademark Image Retrieval System --- p.50Chapter 5.1 --- Problems on Existing Methods --- p.50Chapter 5.1.1 --- Edge Direction Histogram --- p.51Chapter 5.1.2 --- Boundary Based Techniques --- p.52Chapter 5.2 --- Proposed Solution --- p.53Chapter 5.2.1 --- Image Preprocessing --- p.53Chapter 5.2.2 --- Automatic Feature Extraction --- p.54Chapter 5.2.3 --- Approximated Boundary --- p.55Chapter 5.2.4 --- Integration of Shape Features and Query Processing --- p.58Chapter 5.3 --- Experimental Results --- p.58Chapter 5.3.1 --- Experiment 1: Weight Assignment using Genetic Algorithm --- p.59Chapter 5.3.2 --- Experiment 2: Speed on Feature Extraction and Retrieval --- p.62Chapter 5.3.3 --- Experiment 3: Evaluation by Precision --- p.63Chapter 5.3.4 --- Experiment 4: Evaluation by Recall for Deformed Images --- p.64Chapter 5.3.5 --- Experiment 5: Evaluation by Recall for Hand Drawn Query Trademarks --- p.66Chapter 5.3.6 --- "Experiment 6: Evaluation by Recall for Rotated, Scaled and Mirrored Images" --- p.66Chapter 5.3.7 --- Experiment 7: Comparison of Different Integration Methods --- p.68Chapter 5.4 --- Chapter Summary --- p.71Chapter 6 --- Shape-based Chinese Cursive Script Character Image Retrieval System --- p.72Chapter 6.1 --- Comparison to Trademark Retrieval Problem --- p.79Chapter 6.1.1 --- Feature Selection --- p.73Chapter 6.1.2 --- Speed of System --- p.73Chapter 6.1.3 --- Variation of Style --- p.73Chapter 6.2 --- Target of the Research --- p.74Chapter 6.3 --- Proposed Solution --- p.75Chapter 6.3.1 --- Image Preprocessing --- p.75Chapter 6.3.2 --- Automatic Feature Extraction --- p.76Chapter 6.3.3 --- Thinned Image and Linearly Normalized Image --- p.76Chapter 6.3.4 --- Edge Directions --- p.77Chapter 6.3.5 --- Integration of Shape Features --- p.78Chapter 6.4 --- Experimental Results --- p.79Chapter 6.4.1 --- Experiment 8: Weight Assignment using Genetic Algorithm --- p.79Chapter 6.4.2 --- Experiment 9: Speed on Feature Extraction and Retrieval --- p.81Chapter 6.4.3 --- Experiment 10: Evaluation by Recall for Deformed Images --- p.82Chapter 6.4.4 --- Experiment 11: Evaluation by Recall for Rotated and Scaled Images --- p.83Chapter 6.4.5 --- Experiment 12: Comparison of Different Integration Methods --- p.85Chapter 6.5 --- Chapter Summary --- p.87Chapter 7 --- Conclusion --- p.88Chapter 7.1 --- Summary --- p.88Chapter 7.2 --- Future Research --- p.89Chapter 7.2.1 --- Limitations --- p.89Chapter 7.2.2 --- Future Directions --- p.90Chapter A --- A Representative Subset of Trademark Images --- p.91Chapter B --- A Representative Subset of Cursive Script Character Images --- p.93Chapter C --- Shape Feature Extraction Toolbox for Matlab V53 --- p.95Chapter C.l --- central .moment --- p.95Chapter C.2 --- centroid --- p.96Chapter C.3 --- cir --- p.96Chapter C.4 --- ess --- p.97Chapter C.5 --- css_match --- p.100Chapter C.6 --- ecc --- p.102Chapter C.7 --- edgeäždirections --- p.102Chapter C.8 --- fourier-d --- p.105Chapter C.9 --- gen_shape --- p.106Chapter C.10 --- hu7 --- p.108Chapter C.11 --- isclockwise --- p.109Chapter C.12 --- moment --- p.110Chapter C.13 --- normalized-moment --- p.111Chapter C.14 --- orientation --- p.111Chapter C.15 --- resample-pts --- p.112Chapter C.16 --- rectangularity --- p.113Chapter C.17 --- trace-points --- p.114Chapter C.18 --- warp-conv --- p.115Bibliography --- p.11
Simulation to help calibration of a {MEMS} Sensor Network
International audienceThe Smart Surface project aims at designing an integrated micro-manipulator based on an array of micromodules connected with a 2D array topology network. Each micromodule comprises a sensor, an actuator and a processing unit. One of the aims of the processing unit is to differentiate the shape of the part that is put on top of the Smart Surface. From a set of shapes this differentiation is done through a distributed algorithm that we call a criterion. The article presents Sensor Network Calibrator (SNC), a calibrator which allows to parametrize the Smart Surface and to determine the necessary number of sensors required by our Smart Surface. The tests will show that SNC is of great importance for choosing the number of sensors, and therefore to determine the size of the sensors grid
- âŠ