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01.07.2020 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING

Red Green Blue Depth Image Classification Using Pre-Trained Deep Convolutional Neural Network

verfasst von: N. Kumar, N. Kaur, D. Gupta

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 3/2020

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Abstract

Image Classification is one of the eminent challenges in the field of computer vision, and it also acts as a foundation for other tasks such as image captioning, object detection, image coloring, etc. The Convolutional Neural Networks (CNN) techniques have the potency to accomplish image classification for a variety of datasets. With the advancements in technologies, cameras are capturing high-level information such as depth. Therefore, it is essential to incorporate depth information into CNN to provide a better experience of image classification. In this paper, an attempt is made to adapt pre-trained GoogLeNet on Washington RGB-D (RGB-Depth) dataset. Moreover, GoogLeNet is evaluated on depth data that has provided reasonable classification rate on RGB-D dataset. In addition, the paper works on analyzing the impact of pre-processing or resizing of images and batch size on classification accuracy of the model.

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Titel: Red Green Blue Depth Image Classification Using Pre-Trained Deep Convolutional Neural Network
verfasst von: N. Kumar
N. Kaur
D. Gupta
Publikationsdatum: 01.07.2020
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 3/2020
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661820030153

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