Scalable Machine Learning for Visual Data

Recent years have seen a rapid growth of visual data produced by social media, large-scale surveillance cameras, biometrics sensors, and mass media content providers. The unprecedented availability of visual data calls for machine learning methods that are effective and efficient for such large-scale settings. The input of any machine learning algorithm consists of data and supervision. In a large-scale setting, on the one hand, the data often comes with a large number of samples, each with high dimensionality. On the other hand, the unconstrained visual data requires a large amount of supervision to make machine learning methods effective. However, the supervised information is often limited and expensive to acquire. The above hinder the applicability of machine learning methods for large-scale visual data. In the thesis, we propose innovative approaches to scale up machine learning to address challenges arising from both the scale of the data and the limitation of the supervision. The methods are developed with a special focus on visual data, yet they are also widely applicable to other domains that require scalable machine learning methods. Learning with high-dimensionality: The "large-scale" of visual data comes not only from the number of samples but also from the dimensionality of the features. While a considerable amount of effort has been spent on making machine learning scalable for more samples, few approaches are addressing learning with high-dimensional data. In Part I, we propose an innovative solution for learning with very high-dimensional data. Specifically, we use a special structure, the circulant structure, to speed up linear projection, the most widely used operation in machine learning. The special structure dramatically improves the space complexity from quadratic to linear, and the computational complexity from quadratic to linearithmic in terms of the feature dimension. The proposed approach is successfully applied in various frameworks of large-scale visual data analysis, including binary embedding, deep neural networks, and kernel approximation. The significantly improved efficiency is achieved with minimal loss of the performance. For all the applications, we further propose to optimize the projection parameters with training data to further improve the performance. The scalability of learning algorithms is often fundamentally limited by the amount of supervision available. The massive visual data comes unstructured, with diverse distribution and high-dimensionality -- it is required to have a large amount of supervised information for the learning methods to work. Unfortunately, it is difficult, and sometimes even impossible to collect a sufficient amount of high-quality supervision, such as instance-by-instance labels, or frame-by-frame annotations of the videos. Learning from label proportions: To address the challenge, we need to design algorithms utilizing new types of supervision, often presented in weak forms, such as relatedness between classes, and label statistics over the groups. In Part II, we study a learning setting called Learning from Label Proportions (LLP), where the training data is provided in groups, and only the proportion of each class in each group is known. The task is to learn a model to predict the class labels of the individuals. Besides computer vision, this learning setting has broad applications in social science, marketing, and healthcare, where individual-level labels cannot be obtained due to privacy concerns. We provide theoretical analysis under an intuitive framework called Empirical Proportion Risk Minimization (EPRM), which learns an instance level classifier to match the given label proportions on the training data. The analysis answers the fundamental question, when and why LLP is possible. Under EPRM, we propose the proportion-SVM (∝SVM) algorithm, which jointly optimizes the latent instance labels and the classification model in a large-margin framework. The approach avoids making restrictive assumptions on the data, leading to the state-of-the-art results. We have successfully applied the developed tools to challenging problems in computer vision including instance-based event recognition, and attribute modeling. Scaling up mid-level visual attributes: Besides learning with weak supervision, the limitation on the supervision can also be alleviated by leveraging the knowledge from different, yet related tasks. Specifically, "visual attributes" have been extensively studied in computer vision. The idea is that the attributes, which can be understood as models trained to recognize visual properties can be leveraged in recognizing novel categories (being able to recognize green and orange is helpful for recognizing apple). In a large-scale setting, the unconstrained visual data requires a high-dimensional attribute space that is sufficiently expressive for the visual world. Ironically, though designed to improve the scalability of visual recognition, conventional attribute modeling requires expensive human efforts for labeling the detailed attributes and is inadequate for designing and learning a large set of attributes. To address such challenges, in Part III, we propose methods that can be used to automatically design a large set of attribute models, without user labeling burdens. We propose weak attribute, which combines various types of existing recognition models to form an expressive space for visual recognition and retrieval. In addition, we develop category-level attribute to characterize distinct properties separating multiple categories. The attributes are optimized to be discriminative to the visual recognition task over known categories, providing both better efficiency and higher recognition rate over novel categories with a limited number of training samples

Experiments of Image Retrieval Using Weak Attributes

Searching images based on descriptions of image attributes is an intuitive process that can be easily understood by humans and recently made feasible by a few promising works in both the computer vision and multimedia communities. In this report, we describe some experiments of image retrieval methods that utilize weak attributes

Design, synthesis and biological evaluation of isochroman-4-one hybrids bearing piperazine moiety as antihypertensive agent candidates

7,8 Dihydroxy 3 methyl isochromanone 4 XJP is a polyphenolic natural product with moderate antihypertensive activity. T o obtain new agents with stronger potency and safer profile , we employed XJP and naftopidil as the lead compound s t o design and synth esize a novel class of hybrids as antihypertensive candidates, In the present study, a series of hybrids ( 6a r ) of XJP bearing arylpiperazine moiety, which is identified as the pharmacophore of naftopidil, were designed and synthesized as novel α 1 adrenergic receptor antagonists. The biological evaluation showed that target compounds 6c , 6e , 6f , 6g , 6h , 6m and 6q possessed potent in vitro vasodilation potency and α 1 adrenergic receptor antagonistic activity . Furthermore, the most potent compound 6e significantly reduced the systolic and diastolic blood pressure in spontaneously hypertensive rats (SHRs),which was comparable to that of naftopidil, and it had no observable effects on the basal heart rate, suggesting that 6e deserves to be further investigated as a potential clinical candidate for the treatment of hypertension

Antitumor Research of the Active Ingredients from Traditional Chinese Medical Plant Polygonum Cuspidatum

In recent years, the utilization of Chinese native medicine and other plant extracts in the treatment of diseases has attracted extensive attention, especially in the area of malignant tumors. However, lots of herbal remedies active ingredients have not been found or have been discovered but not effectively developed and applied. Therefore, screening new Chinese medicine active components and determining their antitumor effects have become a new breakthrough in the prevention and treatment of tumor disease. In the past years, a large number of studies have demonstrated that Polygonum cuspidatum and its active components like resveratrol showed excellent antitumor activities, including our own antitumor studies about resveratrol in colorectal cancer. The purpose of this review is to summarize the research progress of Chinese herb Polygonum cuspidatum and its active components in tumor diseases and provide theoretical basis for further scientific experiments and clinical applications

Analysis of the Influence Mechanism of Consumers’ Trading Behavior on Reusable Mobile Phones

The aim of this study is to investigate the decision-making mechanism of reusable mobile phone trading behaviors by using the extended theory of planned behavior. In this study, based on the survey data of 964 residents in Beijing, China and structural equation modeling method, the main factors that affect consumers’ reusable mobile phone trading behavior and their degree of influence were analyzed, followed by discussion on decision-making mechanisms. The findings show that consumers’ behavioral selection has been significantly related to four intrinsic subjective factors and fifteen external objective factors, and the combined effect of the latter ones is nearly triple of that of the former ones. Moreover, the observed variables of environmental awareness, information leakage sensitivity, trading convenience and consumer trading returns are the four most significant factors. The impact of active trading behavior is not significant. However, this may be because that there were no great trading rewards, lack of trading awareness and regulations. Finally, the study put forward relevant policy recommendations for improving the comprehensive management of recycling reusable mobile phones, and provides a theoretical reference for improving the recycling rate of reusable mobile phones