ENHANCING SKETCH-BASED IMAGE RETRIEVAL BY CNN SEMANTIC RE-RANKING

Abstract

A re-ranking system to enhance the performance of sketch-predicated image retrieval (SBIR). From the subsisting approaches, the proposed system can leverage category information brought by CNNs to fortify homogeneous attribute quantification between the images. To achieve efficacious relegation, one CNN model is trained for relegation of sketches, another for that of natural images. By training dual CNN models, the semantic information of both the images is captured by deep learning. To quantify the category homogeneous attribute between images, a category homogeneous attribute quantification method are proposed. Category informations are then utilized for reranking. Re-ranking operation first infers the retrieval category of the query sketch and utilizes the category kindred attribute quantification to quantify the category homogeneous attribute between the query and each initial retrieval
result. Determinately, initial retrieval results are re-ranked