Material classification is one of the fundamental problems for multimedia content analysis, computer vision and graphics. Existing efforts mostly focus on extracting representative visual features and training a classifier to recognize unknown materials. Compared with human visual recognition, automatic recognition cannot leverage common sense knowledge regarding material categories and contextual information such as object and scene. In this paper, we propose to first extract such knowledge on material, object and scene from heterogeneous sources, i.e. a public data set of 100 million Flickr images [13] and Bing search results. To improve the material classification task, the knowledge information is further exploited in a probabilistic inference framework. Our method is evaluated on OpenSurfaces [10], the largest public material data set which contains both visual features of physical properties as well as image context information. The quantitative evaluation demonstrates the superior performance of our proposed method.

This paper introduces a novel mathematical approach to surface spectral reflectance estimation in unknown underwater environments using uncalibrated color cameras. The approach derives surface spectral estimates without explicitly modeling the underwater medium characteristics such as light scattering and absorption. The latter two phenomena are dependent upon two parameters, which are the distance of the object from the camera and the depth of the object in water. The proposed approach does not require these parameters to be specified in advance. Spectral models are useful for underwater applications, where subtle differences in color need to be distinguished. Such models are also useful for fusing information from multiple images. We show that the proposed approach yields promising results. 1