Showing papers by "Chang Xu published in 2012"

Diode pumped highly efficient Yb:Lu3Al5O12 ceramic laser

Abstract: A diode pumped ytterbium-doped lutetium aluminum garnet (Yb:LuAG) ceramic laser is reported for the first time to our knowledge. Using the solid state reactive vacuum sintering technique we have successfully fabricated high optical quality Yb:LuAG ceramics. We show that even with an uncoated Yb:LuAG ceramic sample a maximum output power of 7 W could be achieved under 13 W absorbed pump power, and the laser has a slope efficiency as high as 63%. Considering that Yb:LuAG has higher thermal conductivity than Yb:YAG under heavy doping situation, Yb:LuAG ceramic could be an attractive laser gain medium for the high power solid state lasers applications.

Learning to rerank images with enhanced spatial verification

Abstract: Reranking is one of the commonly used schemes to improve the initial ranking performance for content based image retrieval (CBIR). The state-of-the-art reranking methods for CBIR are mainly based on spatial verification and global feature. To mine the complementary properties of different reranking strategies, we combine features representing images from different perspectives with RankSVM to obtain a reranking model to refine the initial ranking list. Besides, compared with traditional spatial verification based methods which measure image similarity only with single inlier's statistical properties, we bind close inlier visual words together to mine more geometric information from images. Through organizing inliers into sequence and computing the relative positions among inliers, we define an efficient similarity measurement with the order consistency between inlier sequences. Experimental results on both Oxford and imageNet datasets demonstrate that our proposed reranking method is effective and promising.

A simple approach to the design of site-level extractors using domain-centric principles

Abstract: We consider the problem of extracting, in a domain-centric fashion, a given set of attributes from a large number of semi-structured websites. Previous approaches [7, 5] to solve this problem are based on page level inference. We propose a distinct new approach that directly chooses attribute extractors for a site using a scoring mechanism that is designed at the domain level via simple classification methods using a training set from a small number of sites. To keep the number of candidate extractors in each site manageably small we use two observations that hold in most domains: (a) imprecise annotators can be used to identify a small set of candidate extractors for a few attributes (anchors); and (b) non-anchor attributes lie in close proximity to the anchor attributes. Experiments on three domains (Events, Books and Restaurants) show that our approach is very effective in spite of its simplicity.