Scene understanding, also known as object category segmentation, is one of the major trends in computer vision. It consists in modeling and inferring object categories through constellations of pixels belonging to a given test image. Many existing solutions suffer from (at least) two major limitations; on the one hand, they only use few (scarce) labeled training data, and on the other hand they rely on context-free learning models, thereby, their potential is not fully explored. In this paper, we adopt a transductive data-driven approach for scene understanding based on kernel machines. The main contribution of this work includes i) a novel transductive approach that exploits both labeled/unlabeled data and jointly learns classifiers and kernel maps for better discrimination, and ii) a context modeling approach that captures semantic as well as geometric relationships between object categories as a part of kernel learning. Experiments conducted in scene understanding, using the SiftFlow dataset, show that the proposed method is competitive against state of the art.

We introduce in this paper a novel image annotation approach based on maximum margin classification and a new class of kernels. The method goes beyond the naive use of existing kernels and their restricted combinations in order to design “model-free” transductive kernels applicable to interconnected image databases. In a first contribution of the method, we learn both a decision criterion and a kernel map that guarantee linear separability in a high dimensional space and good generalization performance. In the second contribution of this work, we extend this class of kernels in order to include label dependency statistics that model contextual relationships between concepts into images. Experiments conducted on MSRC and Corel5k databases show that our method achieves at least comparable results with related state of the art.

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.

We introduce in this work a novel approach for semantic indexing and mental image search. Given semantic concepts defined by few training examples, our formulation is transductive and learns a mapping from an initial ambient space, related to low level visual features, to an output space spanned by a well defined semantic basis where data can be easily explored. With this method, searching for a mental visual target reduces to scanning data according to their coordinates in the learned semantic space. We illustrate the proposed method through our graphical user interface "Spacious", for the purpose of visualization and interactive navigation in generic image databases and satellite images.