Semantic subspace learning for mental search in satellite images

In this paper we introduced a new approach, for mental satellite image search and visualization. We addressed the difficulties when visualizing high dimensional data, and we proposed a semantic subspace learning approach for effective exploration of large scale satellite image data. Our formulation is based on a quadratic programming algorithm that easily extend to large scale data. By plugging the embedding solution of this algorithm into Spacious, users can specify objects of interest, as mixtures of predefined semantics in the learned subspace, and retrieve their targets. As a future work, we are currently investigating the use of other low level features as well as the combination of our semantic subspace learning method with relevance feedback.