Nonparametric Learning with Applications in Pattern Recognition and Image Analysis

非参数学习在模式识别和图像分析中的应用

• 批准号: RGPIN-2015-06412
• 负责人: Krzyzak, Adam
• 金额: $ 2.11万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656057
• 关键词: Nonparametric; Learning; Applications; Pattern; Recognition

Rapid advances in computer technologies for processing visual information create constant demand for new image representation and classification techniques. There is rapidly growing number of applications where data mining and classification play key role. Biometrics is one example where face images, fingerprints or retina scans are used to provide secure access to protected information or to identify an individual. Automatic categorization of pathological cells and cancer malignancy grading is another application where reliable classification systems are needed. Extensive research in machine learning in the last decade demonstrated that good features with strong discriminative power and careful tuning of classifiers lead to significant improvements in classifiers performance. This proposal builds on my earlier work investigating asymptotic properties of various nonparametric classification algorithms. The research that I propose is aimed at automatic extraction of "strong" features leading to good separation of different categories and at designing better classifiers by carefully choosing their parameters in data-dependent way and by investigating their rates of convergence. Knowledge of the rates of convergence will allow the designer to choose the best classifiers in a given application domain and is important for assessing computational complexity of the classification systems. I will investigate new techniques for dimensionality reduction using principal surfaces in high dimensional spaces and I will refine automatic feature selection by means of convolutional and deep belief neural networks. The results of this research will be applied to automatic face recognition,  detection and malignancy grading of cancers, counting of red and white blood cells and detection of malaria in red blood cells.

用于处理视觉信息的计算机技术的快速发展产生了对新的图像表示和分类技术的持续需求。数据挖掘和分类在越来越多的应用中发挥着关键作用。生物识别技术是一个例子，其中面部图像，指纹或视网膜扫描用于提供对受保护信息的安全访问或识别个人。病理细胞和癌症恶性程度分级的自动分类是另一个需要可靠分类系统的应用。在过去十年中，对机器学习的广泛研究表明，具有强大区分能力的良好特征和分类器的仔细调整可以显着提高分类器的性能。这个建议建立在我早期的工作调查各种非参数分类算法的渐近性质。我提出的研究是针对自动提取的“强”功能，导致良好的分离不同的类别，并在设计更好的分类仔细选择他们的参数，在数据依赖的方式，并通过调查他们的收敛速度。了解收敛率将使设计者能够在给定的应用领域中选择最好的分类器，并且对于评估分类系统的计算复杂性非常重要。我将研究在高维空间中使用主表面进行降维的新技术，并将通过卷积和深度信念神经网络来改进自动特征选择。这项研究的结果将应用于自动人脸识别、癌症的检测和恶性分级、红细胞和白色细胞的计数以及红细胞中疟疾的检测。