DeepSketch: Deep convolutional neural networks for sketch recognition and similarity search
Omar Seddati, Stéphane Dupont, Saïd Mahmoudi · 2015
In this paper, we present a system for sketch classification and similarity search. We used deep convolution neural networks (ConvNets), state of the art in the field of image recognition. They enable both classification and medium/highlevel features extraction. We make use of ConvNets features as a basis for similarity search using k-Nearest Neighbors (kNN). Evaluation are performed on the TU-Berlin benchmark. Our main contributions are threefold: first, we use ConvNets in contrast to most previous approaches based essentially on hand crafted features. Secondly, we propose a ConvNet that is both more accurate and lighter/faster than the two only previous attempts at making use of ConvNets for handsketch recognition. We reached an accuracy of 75.42%. Third, we shown that similarly to their application on natural images, ConvNets allow the extraction of medium-level and high-level features (depending on the depth) which can be used for similarity search.1