Content-based image retrieval using stochastic paintbrush transformation

We propose a new content based image retrieval method. The novelty of our approach lies in the applied image similarity measure: unlike traditional features, such as color, texture or shape, our measure is based on a painted representation of the original image. We use paintbrush stroke parameters as features. These strokes are produced by a stochastic paintbrush algorithm which simulates a painting process. Stroke parameters include color, orientation and location. Therefore, it provides information not only about the color content but also about the structural properties of an image. Experimental results on a database of more than 500 images show that the CBIR method using paintbrush features has a higher retrieval rate than methods using color features only.