Attention based image retrieval

Özyer, Gülşah Tümüklü
This thesis proposes a content-based image retrieval (CBIR) system based on the human visual attention, called Attention-based Image Retrieval (ABIR). The proposed ABIR system handles CBIR problem from the perspective of human perception. An efficient visual attention model specific to CBIR problem derived from the computational visual attention model of Itti and Koch is suggested. ABIR system defines the CBIR system as an attention task, where query and images in the database are considered together to extract region of interests. The ABIR system consists of saliency map computing, region extraction, feature extraction and similarity matching steps using the saliency information. Bottom-up Normalization Algorithm, Top-down Normalization Algorithm and Top-down Feature Map Weighting Algorithm are proposed to compute the saliency maps. Bottom-up normalization and top-down normalization algorithms attack the normalization process of Itti-Koch model to compute saliency of images. Bottom-up normalization algorithm computes the normalization parameters from the all images in the dataset. On the other hand, top-down normalization algorithm normalizes the images in the dataset by using query image. Top-down feature map weighting algorithm combines the feature maps of an image in the dataset by using the query image. The features of salient regions are computed by using proposed the saliency-based feature integration algorithm and saliency-based feature selection algorithm. A saliency-based similarity matching algorithm ranks the images with respect to the query image. The proposed ABIR system is tested on STIM and SIVAL object datasets and high resolution airport images. The retrieval results are superior compared to the selected state of the art CBIR systems.
Citation Formats
G. T. Özyer, “Attention based image retrieval,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.