Matching Items (79)
Filtering by

Clear all filters

155963-Thumbnail Image.png
Description
Computer Vision as a eld has gone through signicant changes in the last decade.

The eld has seen tremendous success in designing learning systems with hand-crafted

features and in using representation learning to extract better features. In this dissertation

some novel approaches to representation learning and task learning are studied.

Multiple-instance learning which is

Computer Vision as a eld has gone through signicant changes in the last decade.

The eld has seen tremendous success in designing learning systems with hand-crafted

features and in using representation learning to extract better features. In this dissertation

some novel approaches to representation learning and task learning are studied.

Multiple-instance learning which is generalization of supervised learning, is one

example of task learning that is discussed. In particular, a novel non-parametric k-

NN-based multiple-instance learning is proposed, which is shown to outperform other

existing approaches. This solution is applied to a diabetic retinopathy pathology

detection problem eectively.

In cases of representation learning, generality of neural features are investigated

rst. This investigation leads to some critical understanding and results in feature

generality among datasets. The possibility of learning from a mentor network instead

of from labels is then investigated. Distillation of dark knowledge is used to eciently

mentor a small network from a pre-trained large mentor network. These studies help

in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
156193-Thumbnail Image.png
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable

With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.

A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).

In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.

Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
156084-Thumbnail Image.png
Description
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle

The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.

The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.

In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
156246-Thumbnail Image.png
Description
Diffusion processes in networks can be used to model many real-world processes, such as the propagation of a rumor on social networks and cascading failures on power networks. Analysis of diffusion processes in networks can help us answer important questions such as the role and the importance of each node

Diffusion processes in networks can be used to model many real-world processes, such as the propagation of a rumor on social networks and cascading failures on power networks. Analysis of diffusion processes in networks can help us answer important questions such as the role and the importance of each node in the network for spreading the diffusion and how to top or contain a cascading failure in the network. This dissertation consists of three parts.

In the first part, we study the problem of locating multiple diffusion sources in networks under the Susceptible-Infected-Recovered (SIR) model. Given a complete snapshot of the network, we developed a sample-path-based algorithm, named clustering and localization, and proved that for regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. Then, we considered the case in which only a partial snapshot is observed and proposed a new algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. We proved that OJC can locate all sources with probability one asymptotically with partial observations in the Erdos-Renyi (ER) random graph. Multiple experiments on different networks were done, which show our algorithms outperform others.

In the second part, we tackle the problem of reconstructing the diffusion history from partial observations. We formulated the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and proved the problem is NP hard. Then we proposed a step-by- step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observations. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods.

In the third part, we consider the problem of improving the robustness of an interdependent network by rewiring a small number of links during a cascading attack. We formulated the problem as a Markov decision process (MDP) problem. While the problem is NP-hard, we developed an effective and efficient algorithm, RealWire, to robustify the network and to mitigate the damage during the attack. Extensive experimental results show that our algorithm outperforms other algorithms on most of the robustness metrics.
ContributorsChen, Zhen (Author) / Ying, Lei (Thesis advisor) / Tong, Hanghang (Thesis advisor) / Zhang, Junshan (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
156036-Thumbnail Image.png
Description
Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision: including view-point in activity analysis, articulation in shape analysis, and measurement invariance in non-linear dynamical modeling. The increasing success of these methods is attributed to the complementary information that topology provides, as well

Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision: including view-point in activity analysis, articulation in shape analysis, and measurement invariance in non-linear dynamical modeling. The increasing success of these methods is attributed to the complementary information that topology provides, as well as availability of tools for computing topological summaries such as persistence diagrams. However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets. In this paper theoretically well-grounded approaches to develop novel perturbation robust topological representations are presented, with the long-term view of making them amenable to fusion with contemporary learning architectures. The proposed representation lives on a Grassmann manifold and hence can be efficiently used in machine learning pipelines.

The proposed representation.The efficacy of the proposed descriptor was explored on three applications: view-invariant activity analysis, 3D shape analysis, and non-linear dynamical modeling. Favorable results in both high-level recognition performance and improved performance in reduction of time-complexity when compared to other baseline methods are obtained.
ContributorsThopalli, Kowshik (Author) / Turaga, Pavan Kumar (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
156586-Thumbnail Image.png
Description
Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond

Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning.

Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.
ContributorsAditya, Somak (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Aloimonos, Yiannis (Committee member) / Lee, Joohyung (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2018
156577-Thumbnail Image.png
Description
Network mining has been attracting a lot of research attention because of the prevalence of networks. As the world is becoming increasingly connected and correlated, networks arising from inter-dependent application domains are often collected from different sources, forming the so-called multi-sourced networks. Examples of such multi-sourced networks include critical infrastructure

Network mining has been attracting a lot of research attention because of the prevalence of networks. As the world is becoming increasingly connected and correlated, networks arising from inter-dependent application domains are often collected from different sources, forming the so-called multi-sourced networks. Examples of such multi-sourced networks include critical infrastructure networks, multi-platform social networks, cross-domain collaboration networks, and many more. Compared with single-sourced network, multi-sourced networks bear more complex structures and therefore could potentially contain more valuable information.

This thesis proposes a multi-layered HITS (Hyperlink-Induced Topic Search) algorithm to perform the ranking task on multi-sourced networks. Specifically, each node in the network receives an authority score and a hub score for evaluating the value of the node itself and the value of its outgoing links respectively. Based on a recent multi-layered network model, which allows more flexible dependency structure across different sources (i.e., layers), the proposed algorithm leverages both within-layer smoothness and cross-layer consistency. This essentially allows nodes from different layers to be ranked accordingly. The multi-layered HITS is formulated as a regularized optimization problem with non-negative constraint and solved by an iterative update process. Extensive experimental evaluations demonstrate the effectiveness and explainability of the proposed algorithm.
ContributorsYu, Haichao (Author) / Tong, Hanghang (Thesis advisor) / He, Jingrui (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
156611-Thumbnail Image.png
Description
Handwritten documents have gained popularity in various domains including education and business. A key task in analyzing a complex document is to distinguish between various content types such as text, math, graphics, tables and so on. For example, one such aspect could be a region on the document with a

Handwritten documents have gained popularity in various domains including education and business. A key task in analyzing a complex document is to distinguish between various content types such as text, math, graphics, tables and so on. For example, one such aspect could be a region on the document with a mathematical expression; in this case, the label would be math. This differentiation facilitates the performance of specific recognition tasks depending on the content type. We hypothesize that the recognition accuracy of the subsequent tasks such as textual, math, and shape recognition will increase, further leading to a better analysis of the document.

Content detection on handwritten documents assigns a particular class to a homogeneous portion of the document. To complete this task, a set of handwritten solutions was digitally collected from middle school students located in two different geographical regions in 2017 and 2018. This research discusses the methods to collect, pre-process and detect content type in the collected handwritten documents. A total of 4049 documents were extracted in the form of image, and json format; and were labelled using an object labelling software with tags being text, math, diagram, cross out, table, graph, tick mark, arrow, and doodle. The labelled images were fed to the Tensorflow’s object detection API to learn a neural network model. We show our results from two neural networks models, Faster Region-based Convolutional Neural Network (Faster R-CNN) and Single Shot detection model (SSD).
ContributorsFaizaan, Shaik Mohammed (Author) / VanLehn, Kurt (Thesis advisor) / Cheema, Salman Shaukat (Thesis advisor) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
156783-Thumbnail Image.png
Description
In recent years, deep learning systems have outperformed traditional machine learning systems in most domains. There has been a lot of research recently in the field of hand gesture recognition using wearable sensors due to the numerous advantages these systems have over vision-based ones. However, due to the lack of

In recent years, deep learning systems have outperformed traditional machine learning systems in most domains. There has been a lot of research recently in the field of hand gesture recognition using wearable sensors due to the numerous advantages these systems have over vision-based ones. However, due to the lack of extensive datasets and the nature of the Inertial Measurement Unit (IMU) data, there are difficulties in applying deep learning techniques to them. Although many machine learning models have good accuracy, most of them assume that training data is available for every user while other works that do not require user data have lower accuracies. MirrorGen is a technique which uses wearable sensor data and generates synthetic videos using hand movements and it mitigates the traditional challenges of vision based recognition such as occlusion, lighting restrictions, lack of viewpoint variations, and environmental noise. In addition, MirrorGen allows for user-independent recognition involving minimal human effort during data collection. It also helps leverage the advances in vision-based recognition by using various techniques like optical flow extraction, 3D convolution. Projecting the orientation (IMU) information to a video helps in gaining position information of the hands. To validate these claims, we perform entropy analysis on various configurations such as raw data, stick model, hand model and real video. Human hand model is found to have an optimal entropy that helps in achieving user independent recognition. It also serves as a pervasive option as opposed to a video-based recognition. The average user independent recognition accuracy of 99.03% was achieved for a sign language dataset with 59 different users, 20 different signs with 20 repetitions each for a total of 23k training instances. Moreover, synthetic videos can be used to augment real videos to improve recognition accuracy.
ContributorsRamesh, Arun Srivatsa (Author) / Gupta, Sandeep K S (Thesis advisor) / Banerjee, Ayan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
156887-Thumbnail Image.png
Description
Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for

Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading.

To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time.

Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists.

Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.
ContributorsCao, Jun (Author) / Li, Baoxin (Thesis advisor) / Liu, Huan (Committee member) / Zhang, Yu (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2018