Sketch-based image retrieval SBIR has been extensively studied for decades because sketch is one of the most intuitive ways to describe ideas. However, the large expressional gap between hand-drawn sketches and natural images with small-scale complex structures is the fundamental challenge for SBIR systems. We present a novel framework to efficiently retrieve images with a query sketch based on saliency detection. In order to extract primary contours of the scene and depress textures, a hierarchical saliency map is computed for each image. Object contours are extracted from the saliency map instead of the original natural image. Histograms of oriented gradients HOG are extracted at multiple scales on a dense gradient field. Using a bag-of-visual-words representation and an inverted index structure, our system efficiently retrieves images by sketches. The experimental results conducted on a dataset of 15i¾?k photographs demonstrate that our method performs well for a wide range of natural scenes.
['Xiao Zhang', 'Xuejin Chen']
Robust Sketch-Based Image Retrieval by Saliency Detection