Iconic-Based Retrieval of Grocery Images via Siamese Neural Network

Ciocca, Gianluigi; Napoletano, Paolo; Locatelli, Simone Giuseppe

doi:10.1007/978-3-030-68790-8_22

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…They have also been used for different kinds of problems, such as the evaluation of source code similarity [20], cyber attack detection [21], object tracking [22], chromosome classification [23] and even animal sound classification [24]. More recently and regarding the grocery products, Ciocca et al [25] have applied a Siamese network to capture the relations between iconic and natural images in the Grocery Store Dataset [3]. They evaluated several Siamese models with different Convolutional Neural Networks (CNNs), obtaining the best results with a DenseNet-169 [26] backbone.…”

Section: Solving the One-shot Learning Problemmentioning

confidence: 99%

Improvement of One-Shot-Learning by Integrating a Convolutional Neural Network and an Image Descriptor into a Siamese Neural Network

2021

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Over the last few years, several techniques have been developed with the aim of implementing one-shot learning, a concept that allows classifying images with only a single image per training category. Conceptually, these methods seek to reproduce certain behavior that humans have. People are able to recognize a person they have only seen once, but they are probably not able to do the same with certain animals, such as a monkey. This is because our brains have been trained for years with images of people but not so much of animals. Among the one-shot learning techniques, some of them have used data generation, such as Generative Adversarial Networks (GAN). Other techniques have been based on the matching of descriptors traditionally used for object detection. Finally, one of the most prominent techniques involves using Siamese neural networks. Siamese networks are usually implemented with two convolutional nets that share their weights. They receive two images as input and can detect whether they belong to the same category or not. In the field of grocery products, there has been a lot of research on the one-shot learning problem but not so much on the use of Siamese networks. In this paper, several classifiers are firstly evaluated to decide on a convolutional model to be used with the Siamese and to improve the baseline results obtained in the dataset used. Then, two existing techniques are integrated within the Siamese model: a convolutional net and a Local Maximal Occurrence (LOMO) descriptor. The latter was initially used for the re-identification of people although it has shown its effectiveness to improve the values of a traditional Siamese with only convolutional sisters. The whole network is trained on categories and responds to different categories, showing its strong capacity to deal with the problem of having only one image per category.

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