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

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

Social Turing Tests: Crowdsourcing Sybil Detection

As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools

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

Directed altruistic living donation : what is wrong with the beauty contest?

This paper explores the specific criticism of directed altruistic living organ donation that it creates a ‘beauty contest’ between potential recipients of organs. The notion of the beauty contest in transplantation was recently used by Neidich et al who stated that ‘altruism should be the guiding motivation for all donations, and when it is, there is no place for a beauty contest{\textquoteright}. I examine this beauty contest objection from two perspectives. First, I argue that, when considered against the behaviour of donors, this objection cannot be consistently raised without also objecting to other common aspects of organ donation. I then explore the beauty contest objection from the perspective of recipients, and argue that if the beauty contest is objectionable, it is because of a tension between recipient behaviour and the altruism that supposedly underpins the donation system. I conclude by briefly questioning the importance of this tension in light of the organ shortage

Malicious Content on the Internet: Narrowing Immunity Under the Communications Decency Act

(Excerpt) This Note argues that the scope of CDA § 230, which provides immunity to Internet Service Providers in defamation suits for content posted by third-party users, should be narrowed in circumstances where a website actively solicits malicious content from its users. Part I discusses gossip websites and blogs that are currently immunized by § 230 and analyzes social issues that result from the broad interpretation of the CDA. Part II briefly discusses the law of defamation, followed by a discussion of the policies behind the CDA’s enactment, and the statute’s current scope in regard to defamation suits. Part III analyzes the flaws of the different approaches currently employed by the courts to determine whether a website qualifies for § 230 immunity. Part IV argues that removing websites that solicit malicious content from the scope of the CDA will better uphold the core policies behind its enactment