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- Genre: Doctoral Dissertation
- Creators: Li, Baoxin
- Creators: Rockmaker, Jody
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Bright Summer, a one-movement piece for orchestra, was composed in Arizona, and completed in February 2013. The piece is approximately twelve minutes long. The motivation for writing this piece was the death of my mother the year before, in 2012. The prevailing mood of this work is bright and pleasant, expressing my mother's cheerful personality when she was alive. It also portrays bright summer days which resemble my mother's spirit. Thus, soundscape plays an important role in this work. It depicts summer breeze, rustling sounds of leaves, and, to translate a Korean saying, "high blue skies." This soundscape opens the piece as well as closes it. In the middle section, the fast upbeat themes represent my mother's witty and optimistic personality. The piece also contains the presence of a hymn tune, The Love of God is Greater Far, which informs the motivic content and also functions as the climax of the piece. It was my mother's favorite hymn and we used to sing it together following her conversion to Christianity. The piece contains three main sections, which are held together by transitional material based on the soundscape and metric modulations. Unlike my earlier works, Bright Summer is tonal, with upper tertian harmonies prevailing throughout the piece. However, the opening and closing soundscapes do not have functional harmonies. For example, tertian chords appear and vanish silently, leaving behind some resonant sounds without any harmonic progression. Overall, the whole piece is reminiscent of my mother who lived a beautiful life.
ContributorsKim, JeeYeon (Composer) / DeMars, James (Thesis advisor) / Hackbarth, Glenn (Committee member) / Rogers, Rodney (Committee member) / Levy, Benjamin (Committee member) / Rockmaker, Jody (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Norwegian composer Ola Gjeilo (b. 1978) is highly regarded as an accomplished and prolific composer of choral music. His creative output includes works for chorus, solo piano, and wind symphony. His unique style infuses elements of cinematic music, jazz and improvisation, with particularly intriguing selections of text. This study examines the factors that influence Gjeilo's compositional techniques, and the musical interpretations of conductor Charles Bruffy in his preparation for The Phoenix Chorale's recording Northern Lights: Choral Works by Ola Gjeilo. The eleven works discussed in this study are: The Ground, Evening Prayer, Ubi caritas, Prelude, Northern Lights, The Spheres, Tota pulchra es, Serenity, Phoenix (Agnus Dei), Unicornis captivatur, and Dark Night of the Soul. As a relatively new and young composer, there is very little published literature on Gjeilo and his works. This study provides an intimate glance into the creative process of the composer. By composing in multiple styles and with a variety of inspirational sources, Gjeilo creates a fresh approach toward composition of new choral music. His style is revealed through interviews and numerous collaborations with conductors and performers who have prepared and performed his music, as well through an examination of the eleven works recorded by The Phoenix Chorale.
ContributorsGarrison, Ryan Derrick (Author) / Reber, William (Thesis advisor) / Saucier, Catherine (Committee member) / Rockmaker, Jody (Committee member) / Doan, Jerry (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis presents a new arrangement of Richard Peaslee's trombone solo "Arrows of Time" for brass band. This arrangement adapts Peaslee's orchestration - and subsequent arrangement by Dr. Joshua Hauser for wind ensemble - for the modern brass band instrumentation and includes a full score. A brief biography of Richard Peaslee and his work accompanies this new arrangement, along with commentary on the orchestration of "Arrows of Time", and discussion of the evolution and adaptation of the work for wind ensemble by Dr. Hauser. The methodology used to adapt these versions for the brass band completes the background information.
ContributorsMalloy, Jason Patrick (Author) / Ericson, John (Thesis advisor) / Oldani, Robert (Committee member) / Rockmaker, Jody (Committee member) / Arizona State University (Publisher)
Created2013
Description
The German pianist and composer Johannes Brahms (1883-1897) wrote more than 122 works for a wide variety of ensembles and genres. Despite this remarkable productivity, and his widely heralded talent for innovation and technique as a composer, few of his works have been arranged for solo guitar, and these have focused primarily on his simpler, more melodic works. Conventional wisdom is that his music is "too dense" to be played on the guitar. As a result, there are no arrangements of orchestral works by Brahms in the standard repertoire for the guitar. In arranging Brahms's Serenade in D Major, movt. 1 for the guitar, I provide a counter argument that not all of Brahms's orchestral music is too dense all of the time. In Part I, I provide a brief overview of the history of, and sources for, the Serenade. Part II describes a step-by-step guide through the process of arranging orchestral repertoire for the solo guitar. Part III is an examination of the editing process that utilizes examples from the guitar arrangement of the Serenade in order to illustrate the various techniques and considerations that are part of the editing process. Part IV is a performance edition of the arrangement. In summary, the present arrangement of Brahms's Serenade, op.11 is the beginning of a conversation about why the "guitar world" should be incorporating the music of Brahms into the standard repertoire. The lessons learned, and the technical challenges discovered, should help inform future arrangers and guitar performers for additional compositions by Brahms.
ContributorsLanier, William Hudson (Author) / Koonce, Frank (Thesis advisor) / Micklich, Albie (Committee member) / Rockmaker, Jody (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nelson Rolihlahla Mandela was born July 18, 1918 into the Madiba clan in Mvezo, Transkei, South Africa. Mandela was a lawyer by trade and a freedom fighter who envisioned freedom and equality for all South Africans regardless of race. In 1965, Mandela was imprisoned at Robben Island for twenty-seven years for treason and terrorist activities against the South African apartheid regime: he was assigned prison numbers 46664. In 1992, Mandela was released from prison and two years later not only became the first democratically elected president of South Africa, but also its first black president. "Madiba 46664" is an eight-minute chamber work scored for flute, oboe, clarinet in B-flat, and bassoon; vibraphone, and two percussionists; piano; violins, violas, and celli. The work blends traditional South African rhythms of the drumming culture with elements of Western harmony and form in contrasting textures of homophony, polyphony and antiphony. "Madiba 46664" utilizes Mandela's prison number, birthdate and age (at the time the composition process began in 2013) for the initial generation of meter, rhythm, harmony, melody, and form. The work also shares intercultural concepts that can be seen in the works of three contemporary African composers, South Africans Jeanne Zaidel-Rudolph and Andile Khumalo, and Nigerian Ayo Oluranti. Each section represents a period of Mandela's life as a freedom fighter, a prisoner, and a president. The inspiration stems from the composer's discussions with Mandela soon after his release from prison and prior to his presidency. These lively discussions pertained to the state of traditional music in then apartheid South Africa and led to this creation. The conversations also played a role in the creative process.
ContributorsMabingnai, Collette Sipho (Composer) / DeMars, James (Thesis advisor) / Hackbarth, Glenn (Committee member) / Humphreys, Jere (Committee member) / Rockmaker, Jody (Committee member) / Rogers, Rodney (Committee member) / Arizona State University (Publisher)
Created2014
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.
ContributorsVaradarajan, Srenivas (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Li, Baoxin (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients.
ContributorsZhou, Jiayu (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans (Committee member) / Li, Baoxin (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2014