Image retrieval based on query by saliency content

In this research study we propose the query by saliency content retrieval (QSCR) image retrieval method which is based on human visual attention models. The proposed methodology represents a bottom-up approach considering the human attention, both locally at the level of image regions as well as globally, for the entire image. In the proposed retrieval system, each image is segmented and a set of characteristic features is calculated for each region. A weight is assigned to each region according to its perceived saliency. The proposed methodology considers not only the salient regions but also their context in the image. Localized image similarity is evaluated using an optimization method called the Earth Mover's Distance (EMD). EMD calculates inter-region distances between images and in our algorithm it considers the local saliency as well. A global image saliency, evaluated in terms of spatially-invariant salient edges, is used as an additional term in the optimization search besides the EMD distance. Experimental results are provided for three different image databases containing a wide variety of semantic categories.