Click-through-based Subspace Learning for Image Search
Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong‐Wah Ngo · 2014
One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual keywords with visual image content. Image search engines therefore highly depend on the surrounding texts, which are often noisy or too few to accurately describe the image content. Second, ranking functions are trained on query-image pairs labeled by human labelers, making the annotation intellectually expensive and thus cannot be scaled~up.