Towards solving computer vision problems: datasets, labels, algorithms, and applications

The solution to a supervised computer vision problem consists of an application, algorithm, input data, and a set of human generated labels. Solving these kinds of tasks involves collecting large quantities of data, collecting appropriate labels, and developing machine vision algorithms tailored to the application. Progress on these problems has often benefited from large scale datasets with high fidelity labels. Successful algorithms display a synergy between application goals and the size and quality of the dataset. This thesis presents work highlighting the importance of each component of a supervised vision task.First, the problem of automatically classifying groups of people into social categories is introduced. This problem is called Urban Tribe Classification. To tackle this problem, each individual and the entire group of individuals are modeled. Since this was a newly introduced computer vision problem, a dataset for this task was created. On this dataset, the combined representation of group and individuals outperforms using only the person representations. This model showed promising results for automatic subculture classification.Second, the problem of creating perceptual embeddings based on human similarity judgements is tackled. This work focuses on triplet similarity comparisons of the form ``Is object $i$ more similar to $j$ or $k$?'', which have been useful for computer vision and machine learning applications. Unfortunately, triplet similarity comparisons, like many human labeling efforts, can be prohibitively expensive. This work proposes two techniques for dealing with this obstacle. First, an alternative display for collecting triplets is designed. This display shows a probe image and a grid of query images, allowing the user to collect multiple triplets simultaneously. The display is shown to reduce the cost and time of triplet collection. In addition, higher quality embeddings are created with the improved triplet collection UI. A 10,000-food item dataset of human taste similarity was created using this UI. Second, ``SNaCK,'' a low-dimensional perceptual embedding algorithm that combines human expertise with automatic machine kernels, is introduced. Both parts are complementary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. Finally, the precise localization of key frames of an action is explored. This work focuses on detecting the exact starting frame of a behavior, an important task for neuroscience research. To address this problem, a loss designed to penalize extra and missed action start detections over small misalignments. Recurrent neural networks (RNN) are trained to optimize this loss. The model is shown to reduce the number of false positives, an important criteria defined by the neuroscientist. The performance of the model is evaluated on a new dataset, the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was created for neuroscience research. On this dataset, the proposed model outperforms related approaches and baseline methods using an unstructured loss

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Deep Multi-View Learning for Visual Understanding

PhD ThesisMulti-view data is the result of an entity being perceived or represented from multiple perspectives. Plenty of applications in visual understanding contain multi-view data. For example, the face images for training a recognition system are usually captured by different devices from multiple angles. This thesis focuses on the cross-view visual recognition problems, e.g., identifying the face images of the same person across different cameras. Several representative multi-view settings, from the supervised multi-view learning to the more challenging unsupervised domain adaptive (UDA) multi-view learning, are investigated. Novel multi-view learning algorithms are proposed correspondingly. To be more specific, the proposed methods are based on the advanced deep neural network (DNN) architectures for better handling visual data. However, directly combining the multi-view learning objectives with DNN can result in different issues, e.g., on scalability, and limit the application scenarios and model performance. Corresponding novelties in DNN methods are thus required to solve them. This thesis is organised into three parts. Each chapter focuses on a multi-view learning setting with novel solutions and is detailed as follows: Chapter 3 A supervised multi-view learning setting with two different views are studied. To recognise the data samples across views, one strategy is aligning them in a common feature space via correlation maximisation. It is also known as canonical correlation analysis (CCA). Deep CCA has been proposed for better performance with the non-linear projection via deep neural networks. Existing deep CCA models typically decorrelate the deep feature dimensions of each view before their Euclidean distances are minimised in the common space. This feature decorrelation is achieved by enforcing an exact decorrelation constraint which is computationally expensive due to the matrix inversion or SVD operations. Therefore, existing deep CCA models are inefficient and have scalability issues. Furthermore, the exact decorrelation is incompatible with the gradient based deep model training and results in sub-optimal solution. To overcome these aforementioned issues, a novel deep CCA model Soft CCA is introduced in this thesis. Specifically, the exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL). It can be jointly optimised with the other training objectives. In addition, our SDL loss can be applied to other deep models beyond multi-view learning. Chapter 4 The supervised multi-view learning setting, whereby more than two views exist, are studied in this chapter. Recently developed deep multi-view learning algorithms either learn a latent visual representation based on a single semantic level and/or require laborious human annotation of these factors as attributes. A novel deep neural network architecture, called Multi- Level Factorisation Net (MLFN), is proposed to automatically factorise the visual appearance into latent discriminative factors at multiple semantic levels without manual annotation. The main purpose is forcing different views share the same latent factors so that they are can be aligned at all layers. Specifically, MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned feature, and they can be fused efficiently. The effectiveness of the proposed MLFN is demonstrated by not only the large-scale cross-view recognition problems but also the general object categorisation tasks. Chapter 5 The last problem is a special unsupervised domain adaptation setting called unsupervised domain adaptive (UDA) multi-view learning. It contains a fully annotated dataset as the source domain and another unsupervised dataset with relevant tasks as the target domain. The main purpose is to improve the performance of the unlabelled dataset with the annotated data from the other dataset. More importantly, this setting further requires both the source and target domains are multi-view datasets with relevant tasks. Therefore, the assumption of the aligned label space across domains is inappropriate in the UDA multi-view learning. For example, the person re-identification (Re-ID) datasets built on different surveillance scenarios are with images of different people captured and should be given disjoint person identity labels. Existing methods for UDA multi-view learning problems are aligning different domains either in the raw image space or a feature embedding space for domain alignment. In this thesis, a different framework, multi-task learning, is adopted with the domain specific objectives for a common space learning. Specifically, such common space is proposed to enable the knowledge transfer. The conventional supervised losses can be used for the labelled source data while the unsupervised objectives for the target domain play the key roles in domain adaptation. Two novel unsupervised objectives are introduced for UDA multi-view learning and result in two models as below. The first model, termed common factorised space model (CFSM), is built on the assumptions that the semantic latent attributes are shared between the source and target domains since they are relevant multi-view learning tasks. Different from the existing methods that based on domain alignment, CFSM emphasizes on transferring the information across domains via discovering discriminative latent factors in the proposed common space. However, the multi-view data from target domain is without labels. Therefore, an unsupervised factorisation loss is derived and applied on the common space for latent factors discovery across domains. The second model still learns a shared embedding space with multi-view data from both domains but with a different assumption. It attempts to discover the latent correspondence of multi-view data in the unsupervised target data. The target data’s contribution comes from a clustering process. Each cluster thus reveals the underlying cross-view correspondences across multiple views in target domain. To this end, a novel Stochastic Inference for Deep Clustering (SIDC) method is proposed. It reduces self-reinforcing errors that lead to premature convergence to a sub-optimal solution by changing the conventional deterministic cluster assignment to a stochastic one

A semi-supervised clustering method for payload extraction

Master of ScienceDepartment of Electrical and Computer EngineeringDon M. GruenbacherWilliam H. HsuThis thesis addresses payload extraction, the information extraction task of capturing the text of an article from a formatted document such as a PDF file, and focuses on the application and improvement of density-based clustering algorithms as an alternative or supplement to rule-based methods for this task domain. While supervised learning performs well on classification-based subtasks of payload extraction such as relevance filtering of documents or sections in a collection, the labeled data which it requires for training are often prohibitively expensive (in terms of the time resources of annotators and developers) to obtain. On the other hand, unlabeled data is often relatively easily available without cost in large quantities, but there have not been many ways to exploit them. Semi-supervised learning addresses this problem by using large amounts of unlabeled data, together with the labeled data, to build better classifiers. In this thesis, I present a semi-supervised learning-driven approach for the analysis of scientific literature which either already contains unlabeled metadata, or from which this metadata can be computed. Furthermore, machine learning-based analysis techniques are exploited to make this system robust and flexible to its data environment. The overall goal of this research is to develop a methodology to support the document analysis functions of layout-based document segmentation and section classification. This is implemented within an information extraction system within which the empirical evaluation and engineering objectives of this work are framed. As an example application, my implementation supports detection and classification of titles, authors, additional author information, abstract, and the titles and body of subsections such as ‘Introduction’, ‘Method’, ‘Result’, ’Discussion’, ‘Acknowledgement’, ’Reference’, etc. The novel contribution of this work also includes payload extraction as an intermediate functional stage within a pipeline for procedural information extraction from the scientific literature. My experimental results show that this approach outperforms a state-of-the-field heuristic pattern analysis system on a corpus from the domain of nanomaterials synthesis

Big Data Analytics in Static and Streaming Provenance

Thesis (Ph.D.) - Indiana University, Informatics and Computing,, 2016With recent technological and computational advances, scientists increasingly integrate sensors and model simulations to understand spatial, temporal, social, and ecological relationships at unprecedented scale. Data provenance traces relationships of entities over time, thus providing a unique view on over-time behavior under study. However, provenance can be overwhelming in both volume and complexity; the now forecasting potential of provenance creates additional demands. This dissertation focuses on Big Data analytics of static and streaming provenance. It develops filters and a non-preprocessing slicing technique for in-situ querying of static provenance. It presents a stream processing framework for online processing of provenance data at high receiving rate. While the former is sufficient for answering queries that are given prior to the application start (forward queries), the latter deals with queries whose targets are unknown beforehand (backward queries). Finally, it explores data mining on large collections of provenance and proposes a temporal representation of provenance that can reduce the high dimensionality while effectively supporting mining tasks like clustering, classification and association rules mining; and the temporal representation can be further applied to streaming provenance as well. The proposed techniques are verified through software prototypes applied to Big Data provenance captured from computer network data, weather models, ocean models, remote (satellite) imagery data, and agent-based simulations of agricultural decision making

An Empirical Study of CSS Code Smells in Web Frameworks

Cascading Style Sheets (CSS) has become essential to front-end web development for the specification of style. But despite its simple syntax and the theoretical advantages attained through the separation of style from content and behavior, CSS authoring today is regarded as a complex task. As a result, developers are increasingly turning to CSS preprocessor languages and web frameworks to aid in development. However, previous studies show that even highly popular websites which are known to be developed with web frameworks contain CSS code smells such as duplicated rules and hard-coded values. Such code smells have the potential to cause adverse effects on websites and complicate maintenance. It is therefore important to investigate whether web frameworks may be encouraging the introduction of CSS code smells into websites. In this thesis, we investigate the prevalence of CSS code smells in websites built with different web frameworks and attempt to recognize a pattern of CSS behavior in these frameworks. We collect a dataset of several hundred websites produced by each of 19 different frameworks, collect code smells and other metrics present in the CSS code of each website, train a classifier to predict which framework the website was built with, and perform various clustering tasks to gain insight into the correlations between code smells. Our results show that CSS code smells are highly prevalent in websites built with web frameworks, we achieve an accuracy of 39% in correctly classifying the frameworks based on CSS code smells and metrics, and we find interesting correlations between code smells

Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview

This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture