We demonstrate the use of DeCAF and Improved Fisher Vector image features to
classify clothing texture. The issue of choosing clothes is a problem for the
blind every day. This work attempts to solve the issue with a finger-mounted
camera and state-of-the-art classification algorithms. To evaluate our
solution, we collected 520 close-up images across 29 pieces of clothing. We
contribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, a
camera small enough to be mounted on the finger, and (2) evaluations of
state-of-the-art recognition algorithms applied to our dataset - achieving an
accuracy >95%. Throughout the paper, we will discuss previous work, evaluate
the current work, and finally, suggest the project's future direction.

使用 DeCAF 和改进的 Fisher Vector 图像特征来分类服装纹理。通过使用手指装配的相机和最先进的分类算法，解决盲人日常选择衣物的问题。通过一个能够装配在手指上的 NanEyeGS 相机拍摄的图像数据集 HCTD 以及应用于我们的数据集的最新识别算法评估，取得了超过 95％的准确率。在整篇论文中，我们将讨论先前的工作，评估当前的工作，并最后提出项目的未来方向。

HandSight: 利用戴在手指上的摄像头，使用 DeCAF 和改进的 Fisher Vectors 对衣物颜色和纹理进行分类

HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color  and Texture with a Finger-Mounted Camera

Patterns and textures are defining characteristics of many natural objects: a
shirt can be striped, the wings of a butterfly can be veined, and the skin of
an animal can be scaly. Aiming at supporting this analytical dimension in image
understanding, we address the challenging problem of describing textures with
semantic attributes. We identify a rich vocabulary of forty-seven texture terms
and use them to describe a large dataset of patterns collected in the wild.The
resulting Describable Textures Dataset (DTD) is the basis to seek for the best
texture representation for recognizing describable texture attributes in
images. We port from object recognition to texture recognition the Improved
Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized
texture descriptors not only on our problem, but also in established material
recognition datasets. We also show that the describable attributes are
excellent texture descriptors, transferring between datasets and tasks; in
particular, combined with IFV, they significantly outperform the
state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks.
We also demonstrate that they produce intuitive descriptions of materials and
Internet images.

通过创建 Describable Textures Dataset（DTD），使用 47 种纹理术语，将 IFV 应用于纹理识别，以认识和描述大型野外纹理数据集。这种方法不仅提高了纹理识别数据集的性能，而且也极大地提高了材料识别数据集的性能。