8,704 research outputs found
Image mining: issues, frameworks and techniques
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an
interdisciplinary endeavor that draws upon expertise in
computer vision, image processing, image retrieval, data
mining, machine learning, database, and artificial
intelligence. Despite the development of many
applications and algorithms in the individual research
fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
An information-driven framework for image mining
[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or
image sequence can be processed to identify high-level spatial objects and relationships. To meet
this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval
techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful
patterns/knowledge from each level
On the proliferation of support vectors in high dimensions
The support vector machine (SVM) is a well-established classification method
whose name refers to the particular training examples, called support vectors,
that determine the maximum margin separating hyperplane. The SVM classifier is
known to enjoy good generalization properties when the number of support
vectors is small compared to the number of training examples. However, recent
research has shown that in sufficiently high-dimensional linear classification
problems, the SVM can generalize well despite a proliferation of support
vectors where all training examples are support vectors. In this paper, we
identify new deterministic equivalences for this phenomenon of support vector
proliferation, and use them to (1) substantially broaden the conditions under
which the phenomenon occurs in high-dimensional settings, and (2) prove a
nearly matching converse result
Experimental study of ion heating and acceleration during magnetic reconnection
Ion heating and acceleration has been studied in the well-characterized reconnection layer of the Magnetic Reconnection Experiment [M. Yamada , Phys. Plasmas 4, 1936 (1997)]. Ion temperature in the layer rises substantially during null-helicity reconnection in which reconnecting field lines are anti-parallel. The plasma outflow is sub-Alfvenic due to a downstream back pressure. An ion energy balance calculation based on the data and including classical viscous heating indicates that ions are heated largely via nonclassical mechanisms. The T-i rise is much smaller during co-helicity reconnection in which field lines reconnect obliquely. This is consistent with a slower reconnection rate and a smaller resistivity enhancement over the Spitzer value. These observations show that nonclassical dissipation mechanisms can play an important role both in heating the ions and in facilitating the reconnection process
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