针对视觉映射的鲁棒混合回归模型研究

项目名称： 针对视觉映射的鲁棒混合回归模型研究

项目编号： No.61301270

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 潘力立

作者单位： 电子科技大学

项目金额： 25万元

中文摘要： 视觉映射是一种通过学习输入图像特征和输出变量之间映射函数来估计新样本目标输出值的技术。作为计算机视觉研究领域的一个重要分支，该技术已被广泛应用于汽车辅助驾驶、智能人机接口以及安防等多个产业。混合回归模型近年来被证明为解决视觉映射问题最好的理论模型之一，然而已有模型依然存在一些瓶颈性问题。本项目旨在突破上述瓶颈性问题，拟在如何提高模型鲁棒性，避免局部最优解，应对高维目标输出以及如何选择有效特征等四个方面展开研究工作，进而提高现有混合回归模型在视觉映射应用中的鲁棒性和准确性。本项目的研究意义在于两方面：一方面，它将从理论上克服已有混合回归模型的缺陷，完善了其在建模方面的不足；另一方面，它将通过理论上的进步推动混合回归模型在各类视觉映射问题中的应用，进一步促进视觉映射相关产业的发展。

中文关键词： 视觉映射；混和回归；关联性分析；特征选择；隐空间

英文摘要： Visual mapping is a process of learning the mapping function between input image features and output variable, as such predicting target output through the learned mapping function when given a new sample. As an important branch of computer vision, it has been widely used in the related industries of auto-auxiliary driving, intelligent human computer interface, and security. Recently, mixture of regression models has been demonstrated to achieve the state-of-the-art performance when solving visual mapping problems; however, it also suffers from some limitations. The proposed project aims at circumventing the limitations of existing methods and improving their stability and robustness when applied for visual mapping. It will focus on the following four key research problems: (1) how to improve mixture regression model's robustness, (2) how to avoid it converging to local optimum, (3) how to select helpful features from input for predicting target output, and (4) how to deal with high-dimensional output problem. The significance of this research project is twofold. First, it will overcome the drawbacks of existing mixture regression models and propose improved mixture regression models that are capable of achieving better performance. Second, it will help mixture regression models be applied in more visual mappin

英文关键词： visual mapping；mixture of regressions；relatedness analysis；feature selection；latent space