Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning

In this dissertation, we study visual analysis methods for complex ancient Maya writings. The unit sign of a Maya text is called glyph, and may have either semantic or syllabic significance. There are over 800 identified glyph categories, and over 1400 variations across these categories. To enable fast manipulation of data by scholars in Humanities, it is desirable to have automatic visual analysis tools such as glyph categorization, localization, and visualization. Analysis and recognition of glyphs are challenging problems. The same patterns may be observed in different signs but with different compositions. The inter-class variance can thus be significantly low. On the opposite, the intra-class variance can be high, as the visual variants within the same semantic category may differ to a large extent except for some patterns specific to the category. Another related challenge of Maya writings is the lack of a large dataset to study the glyph patterns. Consequently, we study local shape representations, both knowledge-driven and data-driven, over a set of frequent syllabic glyphs as well as other binary shapes, i.e. sketches. This comparative study indicates that a large data corpus and a deep network architecture are needed to learn data-driven representations that can capture the complex compositions of local patterns. To build a large glyph dataset in a short period of time, we study a crowdsourcing approach as an alternative to time-consuming data preparation of experts. Specifically, we work on individual glyph segmentation out of glyph-blocks from the three remaining codices (i.e. folded bark pages painted with a brush). With gradual steps in our crowdsourcing approach, we observe that providing supervision and careful task design are key aspects for non-experts to generate high-quality annotations. This way, we obtain a large dataset (over 9000) of individual Maya glyphs. We analyze this crowdsourced glyph dataset with both knowledge-driven and data-driven visual representations. First, we evaluate two competitive knowledge-driven representations, namely Histogram of Oriented Shape Context and Histogram of Oriented Gradients. Secondly, thanks to the large size of the crowdsourced dataset, we study visual representation learning with deep Convolutional Neural Networks. We adopt three data-driven approaches: assess- ing representations from pretrained networks, fine-tuning the last convolutional block of a pretrained network, and training a network from scratch. Finally, we investigate different glyph visualization tasks based on the studied representations. First, we explore the visual structure of several glyph corpora by applying a non-linear dimensionality reduction method, namely t-distributed Stochastic Neighborhood Embedding, Secondly, we propose a way to inspect the discriminative parts of individual glyphs according to the trained deep networks. For this purpose, we use the Gradient-weighted Class Activation Mapping method and highlight the network activations as a heatmap visualization over an input image. We assess whether the highlighted parts correspond to distinguishing parts of glyphs in a perceptual crowdsourcing study. Overall, this thesis presents a promising crowdsourcing approach, competitive data-driven visual representations, and interpretable visualization methods that can be applied to explore various other Digital Humanities datasets

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Multi-Label Dimensionality Reduction

abstract: Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.Dissertation/ThesisPh.D. Computer Science 201