Sketch-based Image Retrieval from Millions of Images under Rotation, Translation and Scale Variations

Proliferation of touch-based devices has made sketch-based image retrieval practical. While many methods exist for sketch-based object detection/image retrieval on small datasets, relatively less work has been done on large (web)-scale image retrieval. In this paper, we present an efficient approach for image retrieval from millions of images based on user-drawn sketches. Unlike existing methods for this problem which are sensitive to even translation or scale variations, our method handles rotation, translation, scale (i.e. a similarity transformation) and small deformations. The object boundaries are represented as chains of connected segments and the database images are pre-processed to obtain such chains that have a high chance of containing the object. This is accomplished using two approaches in this work: a) extracting long chains in contour segment networks and b) extracting boundaries of segmented object proposals. These chains are then represented by similarity-invariant variable length descriptors. Descriptor similarities are computed by a fast Dynamic Programming-based partial matching algorithm. This matching mechanism is used to generate a hierarchical k-medoids based indexing structure for the extracted chains of all database images in an offline process which is used to efficiently retrieve a small set of possible matched images for query chains. Finally, a geometric verification step is employed to test geometric consistency of multiple chain matches to improve results. Qualitative and quantitative results clearly demonstrate superiority of the approach over existing methods.