Sketch Image Classification Using Component Based k-NN

Abstract

Recently, several researches on recognition of novice-users' hand-drawn images have been conducted, specifically those on novel feature extraction techniques. Generation of a robust sketch descriptor is one of the most challenging problems in hand-drawn image recognition. Drawing or sketching is a common skill that everyone can do to a varying degree of success. Although an individual may or may not be able to create a beautifully drawn image, any human beings can recognize the shape of each part of a hand-drawn object and the entire object. In this work, we proposed a sketch image classification method that creates an image descriptor from its own components and uses k-NN algorithm for learning/classification. A sketch image can be created simply by drawing some simple geometric shapes. The drawing order, size, and number of the shapes are features that can be extracted and used to recognize an image. After an object in a drawn image has been classified as a car or a human being or anything else, the recognized object can be selected and paired with the motion associated with it. It was found that our proposed method was able to achieve a recognition accuracy rate of 92.33%. We also did a survey with children 7-12 years of age asking them whether they wanted an easy tool that can animate their hand-drawn objects and got almost unanimous affirmative responses from them.