RETRACTED ARTICLE: Automated Hand-drawn sketches retrieval and recognition using regularized Particle Swarm Optimization based deep convolutional neural network

One of the most popular and rising research area of image processing is free hand-drawn sketch recognition and its retrieval. Enlarger number of methods is introduced to retrieve the sketch images but it made few complexity issues and their performance often degraded. So, in this paper, we proposed an effective method of Regularized Particle Swarm Optimization Based Deep Convolutional Neural Network (RPSO-DCNN) algorithm to retrieve the performance of free hand-drawn sketches. In feature extraction, the Regularized Particle Swarm Optimization (RPSO) model that aim is to produce an optimal evolutionary deep learning result. Therefore, the free hand-drawn sketch image classification and its retrieval are performed by Support Vector Machine and Levenshtein distance-based fuzzy k-nearest neighbour (L-FkNN) algorithms. Hence, this work can bring in communication between human and computer. Experimentally, the simulation work of the proposed RPSO-DCNN model is implemented in the running software of MATLAB. The sketch images are chosen from the TU-Berlin dataset, Sketch dataset, SHREC13 dataset, Flickr dataset and Sketchy dataset. Aiming is to facilitate the performance of the proposed RPSO-DCNN model with various kinds of state of art methods such as H-CNN, Fuzzy, CNN, MARQS and TCVD. The experimental result demonstrates that, the proposed RPSO-DCNN accomplish the optimal accuracy with different state-of-art methods.