What is it about?
In this paper, an advanced deep learning model is applied to the CBIR on facial image data. We designed a deep convolution neural network architecture where activation of the convolution layer is used for feature representation and included max-pooling as a feature reduction technique. Furthermore, our model uses partial feature mapping as image descriptor to incorporate the property that facial image contains repeated information.
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Why is it important?
The proposed model is completely unsupervised, and it is fast and accurate in comparison to other deep learning models applied for CBIR over the facial dataset.
Perspectives
The proposed method provided satisfactory results from the experiment, and it outperforms other CNN-based models such as VGG16, Inception V3, ResNet50, and MobileNet.
Dr. PUSHPENDRA SINGH
Inderprastha Engineering College Ghaziabad UP
Read the Original
This page is a summary of: CBIR-CNN: Content Based Image Retrieval on celebrity data using Deep Convolution Neural Network, Recent Advances in Computer Science and Communications, January 2020, Bentham Science Publishers,
DOI: 10.2174/2666255813666200129111928.
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