There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

Data clustering: 50 years beyond K-means

The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories.

/pdf/data-clustering-50-years-beyond-k-means-2sd3ukqpur.pdf

Data Clustering: 50 Years Beyond K-means

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Crowd counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks when handling multiple domains: (1) the models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains, which is called catastrophic forgetting; (2) the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift; (3) it leads to linearly increasing storage overhead, either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new crowd counting task in incremental domain training setting called lifelong crowd counting. Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains. Specifically, we propose a self-distillation learning framework as a benchmark (forget less, count better, or FLCB) for lifelong crowd counting, which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive. A new quantitative metric, normalized Backward Transfer (nBwT), is developed to evaluate the forgetting degree of the model in the lifelong learning process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting

With the large number of Web sites promoting the use of illicit drugs, it has become important to screen these sites for the protection of children on the Internet. Conventional keyword-based approaches are not sufficient because these Web sites often have lots of images and little meaningful words than prices. We propose an AdaBoost-based algorithm for cannabis image recognition. This is the first known attempt at computerized detection of illicit drug Web contents using images. The main technical contributions of our work are two-fold. First, we introduce a novel weak classifier which considers the inherently structural property or ldquoself-similarityrdquo of the cannabis plants. The self-correlation structural characteristics of cannabis can be used as a discriminative property for the purpose of cannabis image recognition. Second, we propose a rapid weak classifier finder, which can efficiently select discriminative weak classifiers from the weak classifier space with little degradation to the classification accuracy. Experiments on real world images have demonstrated improved performance of our method over other methods.

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Boosted cannabis image recognition

Because of its small size and rapid development, the larval zebrafish is an ideal model organism for studying mutant phenotypes using "high-throughput" histological analysis. Although the preparation and subsequent digitization of zebrafish larval histology specimens can be conducted in parallel, the scoring and annotation of the resulting virtual slides is largely manual and therefore rate limiting, which motivates the development of systems for automated characterization of histology images. We present a prototype for automated segmentation and classification of histology images in animal models, with a pilot study focusing on larval zebrafish eye and gut images. We show that the segmentation of the images into regions of individual cell layers can be conducted with good precision using combinations of widely-used image processing operations, and that the resulting classification system, based on a decision tree algorithm, exhibits promising performance.

Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping

The performance of most region-based image retrieval systems depend critically on the accuracy of object segmentation. We propose a region matching approach, unified feature matching (UFM), which greatly increases the robustness of the retrieval system against segmentation related uncertainties. In our retrieval system, an image is represented by a set of segmented regions each of which is characterized by a fuzzy feature reflecting color, texture, and shape properties. The resemblance between two images is then defined as the overall similarity between two families of fuzzy features, and quantified by the UFM measure. The system has been tested on a database of about 60,000 general-purpose images. Experimental results demonstrate improved accuracy and robustness.

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Looking beyond region boundaries: a robust image similarity measure using fuzzified region features

The automatic inference of image semantics is an important but highly challenging research problem whose solutions can greatly benefit content-based image search and automatic image annotation. In this thesis, I present algorithms and statistical models for inferring image semantics and aesthetics from visual content, specifically aimed at improving real-world image search. First, a novel approach to automatic image tagging is presented which furthers the state-of-the-art in both speed and accuracy. The direct use of automatically generated tags in real-world image search is then explored, and its efficacy demonstrated experimentally. An assumption which makes most annotation models misrepresent reality is that the state of the world is static, whereas it is fundamentally dynamic. I explore learning algorithms for adapting automatic tagging to different scenario changes. Specifically, a meta-learning model is proposed which can augment a black-box annotation model to help provide adaptability for personalization, time evolution, and contextual changes. Instead of retraining expensive annotation models, adaptability is achieved through efficient incremental learning of only the meta-learning component. Large scale experiments convincingly support this approach. In image search, when semantics alone yields many matches, one way to rank images further is to look beyond semantics and consider visual quality. I explore the topic of data-driven inference of aesthetic quality of images. Owing to minimal prior art, the topic is first explored in detail. Then, methods for extracting a number of high-level visual features, presumed to have correlation with aesthetics, are presented. Through feature selection and machine learning, an aesthetics inference model is trained and found to perform moderately on real-world data. The aesthetics-correlated visual features are then used in the problem of selecting and eliminating images at the high and low extremes of the aesthetics scale respectively, using a novel statistical model. Experimentally, this approach is found to work well in visual quality based filtering. Finally, I explore the use of image search techniques for designing a novel image-based CAPTCHA, a Web security test aimed at distinguishing humans from machines. Assuming image search metrics to be potential attack tools, they are used in the loop to design attack-resistant CAPTCHAs.

James Z. Wang

Papers

Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd counting

Boosted cannabis image recognition

Automated segmentation and classification of zebrafish histology images for high-throughput phenotyping

Looking beyond region boundaries: a robust image similarity measure using fuzzified region features

Semantics and aesthetics inference for image search: statistical learning approaches