A natural alternative for querying in an image retrieval system is by simply drawing what one has in mind. Indeed, drawing was the primitive means of communication between humans. One of the goals of an image retrieval scenario is to provide users a simple modality for querying. Thereby, a drawing means a simple hand-drawn sketch composed only of strokes that users can do easily, lacking color or texture. Examples of hand-drawn sketches are shown in Figure 1 . This querying modality leads to the sketch based image retrieval problem (SBIR) which is a challenging problem because of two main reasons: (i) images that we want to retrieve are not sketches, (ii) query sketches show certain level of ambiguity by nature that may make a method get confused easily. Consequently, state-of-the-arts SBIR approaches [2, 4] still show low performance. Therefore, taking some ideas of the human visual perception, we present a novel method for sketch based image retrieval. Our method, is based on detecting the occurrences of mid-level patterns on a sketch. To this end, we figure out a set of patterns (learned keyshapes) by means of an unsupervised learning process. We then build a histogram that counts the occurrences of the patterns in the underlying sketch. The histogram is built using soft-voting, spatial division and squared root normalization. We show new state-of-the-art results in two available datasets doubling the precision achieved by current methods. Our proposal consists of two stages ( Figure 2 ): (1) figure out a set of keyshapes, (2) generate the LKS descriptors based on the detected set of keyshapes, that will be used later for similarity search.
In REF , a novel method for describing hand-drawn sketches was proposed by detecting learned keyshapes (LKS).
Sketch based Image Retrieval using Learned KeyShapes (LKS).
BMVC
Computer Science