Learning Compositional Sparse Coding Models for Natural Images

该项目的总体目标是开发自然场景图像的生成模型，以及从自然图像中无监督学习此类模型的相关算法。然后，学习的模型可以用于图像表示和模式识别。该项目中研究的一类特殊模型是组合稀疏编码模型，其中图像由自动学习的模板字典表示，每个模板都是提供图像稀疏编码的小波组合模式。PI和合作者还研究了相关模型，其中模板是非齐次马尔可夫随机场，其能量函数由稀疏编码小波定义。 图像理解是许多现代技术的核心。这也是人类大脑的一个重要功能。图像理解的关键是自动发现图像中的模式。这个项目的目标是开发学习模式或“视觉词”的字典来表示我们环境的图像的方法。学习的字典对于图像理解和对象识别非常有用。

The general goal of this project is to develop generative models for images of natural scenes, as well as associated algorithms for unsupervised learning of such models from natural images. The learned models can then be used for image representation and pattern recognition. A particular class of models investigated in this project are the compositional sparse coding models, where the images are represented by automatically learned dictionaries of templates, and each template is a compositional pattern of wavelets that provide sparse coding of the images. The PI and collaborators also investigate related models where the templates are inhomogeneous Markov random fields whose energy functions are defined by the sparse coding wavelets. Image understanding is at the hearts of many modern technologies. It is also a major function of human brains. The key to image understanding is automatic discovery of patterns in the images. The goal of this project is to develop methods for learning dictionaries of patterns or "visual words" for representing images of our environments. The learned dictionaries can be very useful for image understanding and object recognition.