Sketch-Based Image Retrieval Using Angular Partitioning

This paper presents a novel approach for sketch-based image retrieval based on low-level features. The approach enables measuring the similarity between a full color image and a simple black and white sketched query and needs no cost intensive image segmentation. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Abstract images are obtained using strong edges of the model image and thinned outline of the sketched image. Angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using Fourier transform. The extracted features are scale and rotation invariant and robust against translation. A collection of paintings and sketches (ART BANK) is used for testing the proposed method. The results are compared with three other well-known approaches within the literature. Experimental results show significant improvement in the recall ratio using the new features.