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CREATION
Title : Content-based product image retrieval using squared-hinge loss trained convolutional neural networks
Author :

ARIF RAHMAN (1) Drs. Edi Winarko, M.Sc.,Ph.D. (2) Dr. techn. Khabib Mustofa, S.Si., M.Kom. (3)

Date : 0 2023
Keyword : content-based; convolutional neural network; image retrieval; product image; squared-hinge loss content-based; convolutional neural network; image retrieval; product image; squared-hinge loss
Abstract : Convolutional neural networks (CNN) have proven to be highly effective in large-scale object detection and image classification, as well as in serving as feature extractors for content-based image retrieval. While CNN models are typically trained with category label supervision and softmax loss for product image retrieval, we propose a different approach for feature extraction using the squared-hinge loss, an alternative multiclass classification loss function. First, transfer learning is performed on a pre-trained model, followed by fine-tuning the model. Then, image features are extracted based on the fine-tuned model and indexed using the nearest-neighbor indexing technique. Experiments are conducted on VGG19, InceptionV3, MobileNetV2, and ResNet18 CNN models. The model training results indicate that training the models with squared-hinge loss reduces the loss values in each epoch and reaches stability in less epoch than softmax loss. Retrieval results show that using features from squared-hinge trained models improves the retrieval accuracy by up to 3.7% compared to features from softmax-trained models. Moreover, the squared-hinge trained MobileNetV2 features outperformed others, while the ResNet18 feature gives the advantage of having the lowest dimensionality with competitive accuracy.
Group of Knowledge : Ilmu Komputer
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 2023-IJECE-content based product image retrieval-RWM.pdf
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2 Content-Based Product Image Retrieval Using Squared-Hinge Loss Trained Convolutional Neural Networks-turnitin.pdf
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