Semantic Learning and Web Image Mining with Image Recognition and Classification

Image mining is more than just an extension of data mining to image domain. Web Image mining is a technique commonly used to extract knowledge directly from images on WWW. Since main targets of conventional Web mining are numerical and textual data, Web mining for image data is on demand. There are huge image data as well as text data on the Web. However, mining image data from the Web is paid less attention than mining text data, since treating semantics of images are much more difficult. This paper proposes a novel image recognition and image classification technique using a large number of images automatically gathered from the Web as learning images. For classification the system uses imagefeature- based search exploited in content-based image retrieval(CBIR), which do not restrict target images unlike conventional image recognition methods and support vector machine(SVM), which is one of the most efficient & widely used statistical method for generic image classification that fit to the learning tasks. By the experiments it is observed that the proposed system outperforms some existing search system

New procedures for visualizing data and diagnosing regression models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-103).This thesis presents new methods for exploring data using visualization techniques. The first part of the thesis develops a procedure for visualizing the sampling variability of a plot. The motivation behind this development is that reporting a single plot of a sample of data without a description of its sampling variability can be uninformative and misleading in the same way that reporting a sample mean without a confidence interval can be. Next, the thesis develops a method for simplifying large scatter plot matrices, using similar techniques as the above procedure. The second part of the thesis introduces a new diagnostic method for regression called backward selection search. Backward selection search identifies a relevant feature set and a set of influential observations with good accuracy, given the difficulty of the problem, and additionally provides a description, in the form of a set of plots, of how the regression inferences would be affected with other model choices, which are close to optimal. This description is useful, because an observation, that one analyst identifies as an outlier, could be identified as the most important observation in the data set by another analyst. The key idea behind backward selection search has implications for methodology improvements beyond the realm of visualization. This is described following the presentation of backward selection search. Real and simulated examples, provided throughout the thesis, demonstrate that the methods developed in the first part of the thesis will improve the effectiveness and validity of data visualization, while the methods developed in the second half of the thesis will improve analysts' abilities to select robust models.by Rajiv Menjoge.Ph.D

Access to Digital Cultural Heritage: Innovative Applications of Automated Metadata Generation

Access to Digital Cultural Heritage: Innovative Applications of Automated Metadata Generation Edited by: Krassimira Ivanova, Milena Dobreva, Peter Stanchev, George Totkov Authors (in order of appearance): Krassimira Ivanova, Peter Stanchev, George Totkov, Kalina Sotirova, Juliana Peneva, Stanislav Ivanov, Rositza Doneva, Emil Hadjikolev, George Vragov, Elena Somova, Evgenia Velikova, Iliya Mitov, Koen Vanhoof, Benoit Depaire, Dimitar Blagoev Reviewer: Prof., Dr. Avram Eskenazi Published by: Plovdiv University Publishing House "Paisii Hilendarski" ISBN: 978-954-423-722-6 2012, Plovdiv, Bulgaria First EditionThe main purpose of this book is to provide an overview of the current trends in the field of digitization of cultural heritage as well as to present recent research done within the framework of the project D002-308 funded by Bulgarian National Science Fund. The main contributions of the work presented are in organizing digital content, metadata generation, and methods for enhancing resource discovery. The parts of the book can be downloaded here

Shared Habitats: the MoverWitness Paradigm

Merged with duplicate record 10026.1/642 on 14.02.2017 by CS (TIS)This practice-led research thesis analyses and visualises central components of Authentic Movement, with particular reference to the work of Dr Janet Adler. By contextualising and comparing this improvisation method with modern, post-modern and contemporary movement practices the author describes the emergence of Authentic Movement and distinguishes it from other practices. A new and original viewpoint is adopted and the practice's aesthetic, visual and empathetic characteristics are explored in relationship to and through visual art. The author, a learned Authentic Movement practitioner, critiques, deconstructs and reframes the practice from a visual arts- and performance-based, phenomenological perspective renaming it 'the MoverWitness exchange'. Embedded aspects and skills of the MoverWitness exchange, usually only accessible to firsthand practitioners of the method, are made explicit through research processes of analysis, application and visualisation. Hereby the practice's unique capacity to contain and express binary embodied experiences and concepts is exposed. Resulting insights are crystallised in a distinctive understanding of the MoverWitness exchange that emphasises its suitability as a new learning and/or research methodology for inter- and cross-disciplinary application.Dartington College of Art

06171 Abstracts Collection -- Content-Based Retrieval

From 23.04.06 to 28.04.06, the Dagstuhl Seminar 06171 `Content-Based Retrieval\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Linking animal migration and ecosystem processes: Data-driven simulation of propagule dispersal by migratory herbivores

Animal migration is a key process underlying active subsidies and species dispersal over long distances, which affects the connectivity and functioning of ecosystems. Despite much research describing patterns of where animals migrate, we still lack a framework for quantifying and predicting how animal migration affects ecosystem processes. In this study, we aim to integrate animal movement behavior and ecosystem functioning by developing a predictive modeling framework that can inform ecosystem management and conservation.We propose a framework to model individual-level migration trajectories between populations' seasonal ranges as well as the resulting dispersal and fate of propagules carried by the migratory animals, which can be calibrated using empirical data at every step of the modeling process. As a case study, we applied our framework to model the spread of guava seeds, Psidium guajava, by a population of migratory Galapagos tortoises, Chelonoidis porteri, across Santa Cruz Island. Galapagos tortoises are large herbivores that transport seeds and nutrients across the island, while Guava is one of the most problematic invasive species in the Galapagos archipelago.Our model can predict the pattern of spread of guava seeds alongside tortoises' downslope migration range, and it identified areas most likely to see establishment success. Our results show that Galapagos tortoises' seed dispersal may particularly contribute to guava range expansion on Santa Cruz Island, due to both long gut retention time and tortoise's long-distance migration across vegetation zones. In particular, we predict that tortoises are dispersing a significant amount of guava seeds into the Galapagos National Park, which has important consequences for the native flora.The flexibility and modularity of our framework allow for the integration of multiple data sources. It also allows for a wide range of applications to investigate how migratory animals affect ecosystem processes, including propagule dispersal but also other processes such as nutrient transport across ecosystems. Our framework is also a valuable tool for predicting how animal-mediated propagule dispersal can be affected by environmental change. These different applications can have important conservation implications for the management of ecosystems that include migratory animals

Mapping of multiple muscles with transcranial magnetic stimulation: Absolute and relative test-retest reliability

The spatial accuracy of transcranial magnetic stimulation (TMS) may be as small as a few millimeters. Despite such great potential, navigated TMS (nTMS) mapping is still underused for the assessment of motor plasticity, particularly in clinical settings. Here, we investigate the within‐limb somatotopy gradient as well as absolute and relative reliability of three hand muscle cortical representations (MCRs) using a comprehensive grid‐based sulcus‐informed nTMS motor mapping. We enrolled 22 young healthy male volunteers. Two nTMS mapping sessions were separated by 5–10 days. Motor evoked potentials were obtained from abductor pollicis brevis (APB), abductor digiti minimi, and extensor digitorum communis. In addition to individual MRI‐based analysis, we studied normalized MNI MCRs. For the reliability assessment, we calculated intraclass correlation and the smallest detectable change. Our results revealed a somatotopy gradient reflected by APB MCR having the most lateral location. Reliability analysis showed that the commonly used metrics of MCRs, such as areas, volumes, centers of gravity (COGs), and hotspots had a high relative and low absolute reliability for all three muscles. For within‐limb TMS somatotopy, the most common metrics such as the shifts between MCR COGs and hotspots had poor relative reliability. However, overlaps between different muscle MCRs were highly reliable. We, thus, provide novel evidence that inter‐muscle MCR interaction can be reliably traced using MCR overlaps while shifts between the COGs and hotspots of different MCRs are not suitable for this purpose. Our results have implications for the interpretation of nTMS motor mapping results in healthy subjects and patients with neurological conditions

Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy

AN INTERFACE FOR IMAGE RETRIEVAL AND ITS EXTENSION TO VIDEO RETRIEVAL

National audienceSemantic video retrieval is still an open problem. While many works exist in analyzing the video contents, few ones present the retrieval results to the users and interact with him/her. In this article, firstly, we propose a 2D graphic interface adapted to the problem of image retrieval that enables a bidirectional communication: from the system towards the user to visualize the current research results and from the user towards the system so that the user can provide some relevance feedback information to refine his/her query. In this interface, the visualization shows the image query in the middle of the screen and the result images in a 2D plan with distances showing the similarity measures between images and the query. We propose also a method of relevance feedback in form of validation, in this interface, for image retrieval. This approach has been implemented and tested with different image databases. Secondly, we analyze the extension of this approach for video retrieval. For this, we extract the key frames from video and use them to represent the research results of video as well as to do the relevance feedback