Object category recognition with large training sets

计算机视觉领域的一个重要的长期目标是使计算机能够识别图像中出现的广泛类别的物体（例如自行车、奶牛或人）。虽然这对一个人来说似乎毫不费力，但目前计算机视觉系统的性能仍然远远低于人类视觉。这个问题的解决方案对许多应用程序都很重要，例如汽车驾驶辅助、网络图像搜索或视障人士的辅助。我们认为，现有系统性能有限的一个主要原因是它们使用的训练数据不足。这项研究将开发用于处理更大的图像训练集的方法，例如每个图像类别有数以万计的图像，而不是目前使用的几百个。这将需要新的学习方法和快速算法来扩展到大量数据。我们将通过开发新的特征选择方法来实现可扩展性的其他改进，其中只有一小部分最佳潜在视觉特征被选择用于每个特定的识别任务。我们的研究小组已经开发了一些被广泛使用的软件来加速类似的问题，比如近似近邻快速库（FLANN）。我们打算以我们在这些领域的专业知识为基础，利用大量不同的本地特征，并将训练集的规模至少提高两个数量级。初步实验表明，这将大大提高识别的准确性，并使许多对社会有重要意义的新应用成为可能。

An important long-term goal for the field of computer vision is to enable a computer to recognize broad categories of objects (for example bicycles, cows, or people) whenever they appear in images. While this seems effortless for a person, the current performance of computer vision systems remains far below that of human vision. The solution to this problem is important for numerous applications, such as automobile driver assistance, image search on the web, or aids to the visually impaired. We believe that a major reason for the limited performance of existing systems is that they use inadequate training data. This research will develop approaches for working with far larger training sets of images, such as tens of thousands of images per image category rather than the few hundred currently used. This will require new learning methods and fast algorithms for scaling up to large amounts of data. We will achieve other improvements in scalability by developing new approaches to feature selection, in which only a small subset of the best potential visual features are selected for each particular discrimination task. Our research group has already developed some widely used software for speeding up similar problems, such as the Fast Library for Approximate Nearest Neighbours (FLANN). We intend to build on our expertise in these areas to exploit large sets of diverse local features and scale training set sizes by at least two orders of magnitude over current capabilities. Preliminary experiments indicate that this will give a large improvement in the accuracy of recognition and enable many new applications of importance to society.