The National Library of Medicine (NLM), an institute in the National Institutes of Health (NIH), maintains a collection of 17,000 digitized spine X-ray images obtained from the second National Health and Nutrition Examination Survey (NHANES II). Research effort has been devoted to develop a web-accessible retrieval system that allows retrieval of images from the NHANES II database on relevant and frequently found pathologies. A comprehensive and successful image retrieval system requires effective image representation and matching methods, relevance feedback algorithms to incorporate user opinions, and efficient indexing schemes for fast access to image databases. This dissertation studies and develops approaches for all of the above areas within the context of content-Based Image Retrieval (CBIR) of spine X-ray images from the NHANES II collection. Shape is an important characteristic for describing pertinent pathologies in various types of medical images, including spine X-ray images. Retrieving images with shapes similar to a specific user query can be useful for finding pathologies exhibited in images in large survey collections. In this work, vertebral outlines are extracted for image retrieval using shape matching methods to detect the presence of anterior osteophytes. The Multiple Open Triangle (MOT) shape representation method is proposed for partial shape matching (PSM), and a Corner-Guided Dynamic Programming (DP) strategy is developed to search partial intervals for matching comparison based on a 9-point model marked by a board-certified radiologist. The MOT method demonstrates higher retrieval accuracy compared to other approaches and the retrieval speed is improved significantly through the use of Corner-Guided DP. Computer-calculated low-level image features fall short when imitating high-level human visual perception. Relevance Feedback (RF) attempts to bridge the gap between the two by analyzing and employing user feedback. The need for overcoming this gap is more evident in medical image retrieval. Existing RF approaches are analyzed and a weight-updating formula for RF is developed. A hybrid retrieval approach is proposed that utilizes both CBIR with RF and RF history. This hybrid approach uses short-term memory to store the feedback history, which contributes to the retrieval results and helps select images for user feedback. An approximate 20% average increase in retrieval recall percentage is achieved within two RF iterations. Efficient indexing methods are desired for fast database access. An agglomerative clustering algorithm is adopted to pre-index the database based on pre-calculated pair-wise distances between indexed parts. Retrieval with this pre-indexing procedure is shown to offer faster retrieval and maintain a comparable recall percentage.

In recent years, multimedia identification has become important as the volume of digital media has dramatically increased. With music files, one method of identification is audio fingerprinting. The underlying method for most algorithms is the Fourier transform. However, due to a lack of temporal resolution, these algorithms rely on the short-time Fourier transform. We propose an audio fingerprinting algorithm that uses a wavelet transform, which has good temporal resolution. In this thesis, we examine the basics of certain topics that are needed in understanding audio fingerprinting techniques. We also look at a brief history of work done in this field. We introduce a new algorithm, called the Hokua algorithm. We developed Hokua to take advantage of certain properties of the wavelet transform. The algorithm uses coefficient peaks of wavelet transforms to identify a sample query. The various algorithms are compared.

Under the direction of Dr. Lynn Henrichsen, a group of students has developed numerous units for the Basic Training and Resources for Teaching English to Speakers of Other Languages (BTRTESOL) program. This program is currently located on a website and will someday be published in book and DVD format. These units provide general training for novice teachers who teach domestically or abroad. With little or no training, volunteer English language teachers are often left with questions that BTRTESOL strives to answer in its 52 units. As this audience may or may not have university education or heavy commitment, these units are kept short and to the point with easy to read and understand language. The program uses a minimalist approach so each unit includes only "The least you should know" while connecting users to additional resources in a "where to go to learn more" section. This master's project describes the creation, evaluation and revision of two units for the BTRTESOL program, "Content-Based Language Classes" and "Multiple Skills in One Class." The first unit introduces the idea of integrating content teaching and language teaching into one course. In addition, it explains different types of scaffolding and teaching techniques that will aid novice teachers in creating successful content-based instruction courses. The second unit will help teachers to integrate different linguistic modalities into one course. Suggestions on how to pick themes, manage class time, and plan lessons are addressed

An image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. To control the image mosaic creation process, several parameters are used within the system. Each parameter affects the overall mosaic quality, as well as required processing time, in its own unique way. A detailed analysis is performed to evaluate each parameter individually. Additionally, this work proposes two novel ways by which to evaluate the quality of an image mosaic in a quantitative way. One method focuses on the perceptual color accuracy of the mosaic reproduction, while the other concentrates on edge replication. Both measures include preprocessing to take into account the unique visual features present in an image mosaic. Doing so minimizes quality penalization due the inherent properties of an image mosaic that make them visually appealing.

Yes / The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes. / Supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904).

This study was designed to explore written responses of dialogue journals in a fourth-grade social studies classroom to better understand individuals' meaning-making responses during content-based lessons. The Transactional Theory of Literacy acknowledges that readers generate individualized experiences as they transact with literacy. Although Rosenblatt focused explicitly on the transactions readers make with text, this study expands the idea of these transactions to the more current, unbounded definition of text. Writing could be the tool used for students to record these transactions that lead to their continuously changing, individualized understandings. Through journals, students conversed with one another using written dialogue in the continued generation or restructuring of existing understandings in response to exposure of a content-specific text. The following research questions were addressed in the study: How do written responses of fourth-grade students made in dialogue journals express students' understandings of content-based lessons? 2) To what extent do dialogue journals motivate students in content-based lessons? Analysis of dialogue journals showed evidence of varying levels of understanding, the effective use of journals as a communication tool, and differences in statement types depending on journal audience and content materials used. The MUSIC Model Inventory (Jones, 2009) used to assess perceptions of motivational constructs related to use of dialogue journals in social studies lessons yielded positive results for all constructs measured. Therefore, the results of the study including word count findings, qualitative journal analysis, and observational files clearly showed evidence of dialogue journals being a motivating way of having students express their understandings of content-based texts. / Ph. D.

In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and jl{events surrounding} certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the task. We propose a four-step human-AI collaboration workflow to support the jl{discovery} and clustering of these backdrops. Focusing on the painted backdrops of the American Civil War (1861 -- 1865), we present Backdrop Explorer, a content-based image retrieval (CBIR) system incorporating computer vision and novel user interactions. We evaluated Backdrop Explorer on nine users of diverse experience levels and found that all were able to effectively utilize Backdrop Explorer to find photos with similar backdrops. We also document current practices and pain points in Civil War backdrop research through user interviews. Finally, we discuss how our findings and workflow can be applied to other topics and domains. / Master of Science / In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and events surrounding certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the largely manual task. We present Backdrop Explorer, a reverse image search system that helps users discover and subsequently group photos with similar backdrops. We evaluated the system and found that it effectively supported the tasks. We also document current practices and pain points in Civil War backdrop research. Finally, we discuss how our findings and system can be applied to other domains.

Rekommendationssystem används överallt. På populära plattformar såsom Netflix och Amazon får du alltid rekommendationer på vad som är nästa lämpliga film eller inköp, baserat på din personliga profil. Detta sker genom korsreferering mellan användare och produkter för att finna sannolika mönster. Syftet med studien har varit att jämföra de två prevalenta tillvägagångssätten att skapa rekommendationer, på en annorlunda datamängd, där ”best practice” inte nödvändigtvis är tillämpbart. Som följd därav, har jämförelse gjorts på effektiviteten av Content-based Filtering kontra Collaborative Filtering, på Steams spelplattform, i syfte att etablera potential för en bättre lösning. Detta angreps genom att samla in data från Steam; Bygga en Content-based Filtering motor som baslinje för att representera Steams nuvarande rekommendationssystem, samt en motsvarande Collaborative Filtering motor, baserad på en standard-implementation, att jämföra mot. Under studiens gång visade det sig att Content-based Filtering prestanda initiellt växte linjärt medan spelarbasen på ett givet spel ökade. Collaborative Filtering däremot hade en exponentiell prestationskurva för spel med få spelare, för att sedan plana ut på en nivå som prestationsmässigt överträffade jämförelsmetoden. Den praktiska signifikansen av dessa resultat torde rättfärdiga en mer utbredd implementering av Collaborative Filtering även där man normalt avstår till förmån för Content-based Filtering då det är enklare att implementera och ger acceptabla resultat. Då våra resultat visar på såpass stor avvikelse redan vid basmodeller, är det här en attityd som mycket väl kan förändras. Collaborative Filtering har varit sparsamt använt på mer mångfacetterade datamängder, men våra resultat visar på potential att överträffa Content-based Filtering med relativt liten insats även på sådana datamängder. Detta kan gynna alla inköps- och community-kombinerade plattformar, då det finns möjlighet att övervaka användandet av inköpen i realtid, vilket möjliggör för justeringar av de faktorer som kan visa sig resultera i felrepresentation. / The use of recommender systems is everywhere. On popular platforms such as Netflix and Amazon, you are always given new recommendations on what to consume next, based on your specific profiling. This is done by cross-referencing users and products to find probable patterns. The aims of this study were to compare the two main ways of generating recommendations, in an unorthodox dataset where “best practice” might not apply. Subsequently, recommendation efficiency was compared between Content Based Filtering and Collaborative Filtering, on the gaming-platform of Steam, in order to establish if there was potential for a better solution. We approached this by gathering data from Steam, building a representational baseline Content-based Filtering recommendation-engine based on what is currently used by Steam, and a competing Collaborative Filtering engine based on a standard implementation. In the course of this study, we found that while Content-based Filtering performance initially grew linearly as the player base of a game increased, Collaborative Filtering’s performance grew exponentially from a small player base, to plateau at a performance-level exceeding the comparison. The practical consequence of these findings would be the justification to apply Collaborative Filtering even on smaller, more complex sets of data than is normally done; The justification being that Content-based Filtering is easier to implement and yields decent results. With our findings showing such a big discrepancy even at basic models, this attitude might well change. The usage of Collaborative Filtering has been used scarcely on the more multifaceted datasets, but our results show that the potential to exceed Content-based Filtering is rather easily obtainable on such sets as well. This potentially benefits all purchase/community-combined platforms, as the usage of the purchase is monitorable on-line, and allows for the adjustments of misrepresentational factors as they appear.

Content-based publish-subscribe is emerging as a communication paradigm able to meet the demands of highly dynamic distributed applications, such as those made popular by mobile computing and peer-to-peer networks. Nevertheless, the available systems implementing this communication model are still unable to cope efficiently with dynamic changes to the topology of their distributed dispatching infrastructure. This hampers their applicability in the aforementioned scenarios. This thesis addresses this problem and presents a complete approach to the reconfiguration of content-based publish-subscribe systems. In Part I, it proposes a layered architecture for reconfigurable publish-subscribe middleware consisting of an overlay, a routing, and an event-recovery layer. This architecture allows the same routing components to operate in different types of dynamic network environments, by exploiting different underlying overlays. Part II addresses the routing layer with new protocols to manage the recon- figuration of the routing information enabling the correct delivery of events to subscribers. When the overlay changes as a result of nodes joining or leaving the network or as a result of mobility, this information is updated so that routing can adapt to the new environment. Our protocols manage to achieve this with as little overhead as possible. Part III addresses the overlay layer and proposes two novel approaches for building and maintaining a connected topology in highly dynamic network sce- narios. The protocols we present achieve this goal, while managing node degree and keeping reconfigurations localized when possible. These properties allow our overlay managers to be applied not only in the context of publish-subscribe mid- dleware but also as enabling technologies for other communication paradigms like application-level multicast. Finally, the thesis integrates the overlay and routing layers into a single frame- work and evaluates their combined performance both in wired and in wireless scenarios. Results show that the optimizations provided by our routing reconfig- uration protocols allow the middleware to achieve very good performance in such networks. Moreover, they highlight that our overlay layer is able to optimize this performance even further, significantly reducing the network traffic generated by the routing layer. The protocols presented in this thesis are implemented in the REDS middle- ware framework developed at Politecnico di Milano. Their use enables REDS to operate efficiently in dynamic network scenarios ranging from large-scale peer-to- peer to mobile ad hoc networks.

Transmission of video content over wireless access networks (in particular, Wireless Local Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing video quality prediction models. The main aim of the project is the development of novel and efficient models for video quality prediction in a non-intrusive way for low bitrate and resolution videos and to demonstrate their application in QoS-driven adaptation schemes for mobile video streaming applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content type was found to be the most important parameter. (3) Efficient regression-based and artificial neural network-based learning models were developed for video quality prediction over WLAN and UMTS access networks. The models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and optimization in network planning and content provisioning for network/service providers.(4) The applications of the proposed regression-based models were investigated in (i) optimization of content provisioning and network resource utilization and (ii) A new fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks. (5) Finally, Internet-based subjective tests that captured distortions caused by the encoder and the wireless access network for different types of contents were designed. The database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.