5 research outputs found

    Content-based product image retrieval using squared-hinge loss trained convolutional neural networks

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    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

    Design and implementation of a distributed system for content-based image retrieval

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    The aim of this work is to design and implement a distributed system for content-based image retrieval on very large image databases. To realize this system, a standard full-text search engine has been used. In particular, the system has been developed with the open source software Elasticsearch which, in turn, is built on top of Apache LuceneTM, a widely used full-text search engine Java library. In order to allow the full-text search engine to perform similarity search, we used Deep Convolutional Neural Network Features extracted from the images of the dataset and encoded as standard text. Given the distributed nature of Elasticsearch, the index can be split and spread among several nodes. This makes it easy to parallelize the search, thus leading to a significant performance enhancement. All the experiments have been conducted on the Yahoo Flickr Creative Commons 100M dataset, publicly available and composed of about 100 million of tagged images. A web-based GUI has been designed to allow the user to perform both textual and visual similarity search on the dataset of images
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