A patch-based sparse representation for sketch recognition

Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. One of the most fundamental problems is how to effectively describe a sketch image. Many existing descriptors, such as histogram of oriented gradients (HOG) and shape context (SC), have achieved great success. Moreover, some works have attempted to design features specifically engineered for sketches, such as symmetric-aware flip invariant sketch histogram (SYM-FISH). We present a novel patch-based sparse representation (PSR) for describing sketch image and it is evaluated under a sketch recognition framework. Extensive experiments on a large scale human drawn sketch dataset demonstrate the effectiveness of the proposed method.