Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach

Abstract

Image-based object recognition is a well-studied topic in the field of computer vision. Features extraction for hand-drawn sketch recognition and retrieval become increasingly popular among the computer vision researchers. Increasing use of touchscreens and portable devices raised the challenge for computer vision community to access the sketches more efficiently and effectively. In this article, a novel deep convolutional neural network-based (DCNN) framework for hand-drawn sketch recognition, which is composed of three wellknown pre-trained DCNN architectures in the context of transfer learning with global average pooling (GAP) strategy is proposed. First, an augmented-variants of natural images was generated and sum-up with TU-Berlin sketch images to all its corresponding 250 sketch object categories. Second, the features maps were extracted by three asymmetry DCNN architectures namely, Visual Geometric Group Network (VGGNet), Residual Networks (ResNet) and Inception-v3 from input images. Finally, the distinct features maps were concatenated and the features reductions were carried out under GAP layer. The resulting feature vector was fed into the softmax classifier for sketch classification results. The performance of proposed framework is comprehensively evaluated on augmented-variants TU-Berlin sketch dataset for sketch classification and retrieval task. Experimental outcomes reveal that the proposed framework brings substantial improvements over the state-of-the-art methods for sketch classification and retrieval.

Extraction of Point-of-Interest in Multispectral Images for Face Recognition

“…When the database contains enough images, a Deeplearning solution will be proposed in future work, as many research projects are moving towards this solution. In 2019 Shaukat Hayat et al [35], proposed to use deep CNN-based features for Hand-Drawn sketch recognition via Transfer Learning Approach. Xiang Wang et al introduced also method of privacy-preserving face recognition [36] where the convolutional neural network is used for face feature extraction.…”

Entropy information‐based heterogeneous deep selective fused features using deep convolutional neural network for sketch recognition

“…This model was evaluated on multiple sketch datasets and achieved a competitive performance. Another study [10] develops a DCNN framework using a transfer learning approach for sketch classification. This framework uses augmented variants with sketch images and extracts feature maps to construct a feature vector based on the global average pooling (GAP) layer.…”

“…However, low‐rank retrieval results on the sketchy dataset are illustrated in Figure 15. Additionally, the proposed scheme also evaluated and checked its performance with photos and sketch images [10] other than used in training and validation.…”

“…However, in some cases, it corresponds to a final feature representations and then feeds them into the classifier for a final decision. In this vein, utilisation of deep learning approaches to extract deep features from input images [10,11], and/or deep architecture [12], particularly designed for sketch recognition also achieved state-of-the-art performance.…”

“…Each level of feature has its individual strength and potential. The most recent studies [10,19], utilised a combination of multiple layers to get more robust feature representation as compared to single-layer performance.…”