Content Based Image Retrieval by Convolutional Neural Networks

Hamreras, Safa; Benítez-Rochel, Rafaela; Boucheham, Bachir; Molina‐Cabello, Miguel A.; López‐Rubio, Ezequiel

doi:10.1007/978-3-030-19651-6_27

Cited by 7 publications

(3 citation statements)

References 19 publications

(24 reference statements)

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“…This enormous volume of multimedia data is utilized in various industries, including digital forensics, electronic games, archaeology, video, satellite data and still image repositories, and medical treatment. This rapid growth has generated a continuous need for image retrieval systems that operate on a large scale [1]. For large image databases, the traditional text-based image extraction approach seems ineffective.…”

Section: Introductionmentioning

confidence: 99%

Content-based image retrieval based on corel dataset using deep learning

Hassan

Sultani

Dhannoon

2023

IJ-AI

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A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Content-based image retrieval based on corel dataset using deep learning

Hassan

Sultani

Dhannoon

2023

IJ-AI

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“…The employment of different base learners generation processes and/or different combination schemes leads to different ensemble methods. Unlike our previous work [18] which is based on the use of individual CNNs, in this paper we propose to build an ensemble of Convolutional Neural Networks to identify the most relevant images in the database for a query image. To describe in detail the proposed model as well as the experimental results, the article is organised as follows.…”

Section: Introductionmentioning

confidence: 99%

Content based image retrieval by ensembles of deep learning object classifiers

Hamreras

Boucheham

Molina‐Cabello

et al. 2020

ICA

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Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this paper, we address this paradigm within content based image retrieval (CBIR). We propose to build an ensemble of convolutional neural networks (CNNs), either by training the CNNs on different bags of images, or by using CNNs trained on the same dataset, but having different architectures. Each network is used to extract the class probability vectors from images to use them as representations. The final image representation is then generated by combining the extracted class probability vectors from the built ensemble. We show that the use of CNN ensembles is very efficient in generating a powerful image representation compared to individual CNNs. Moreover, we propose an Averarge Query Expansion technique for our proposal to enhance the retrieval results. Several experiments were conducted to extensively evaluate the application of ensemble learning in CBIR. Results in terms of precision, recall, and mean average precision show the outperformance of our proposal compared to the state of the art.

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“…Deep learning has demonstrated its performance in large-scale visual recognition in recent years. Some researchers also apply this technology to image retrieval tasks (Babenko et al, 2014; Hamreras et al, 2019; Lin et al, 2015). Due to the successful applications of deep learning in CBIR, we try to find an effective image retrieval framework in the field of deep learning for bronze inscriptions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for fast bronze inscription image retrieval

Wang

Zheng

Chen

2020

Journal of Chinese Writing Systems

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Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive accuracy.

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Content Based Image Retrieval by Convolutional Neural Networks

Cited by 7 publications

References 19 publications

Content-based image retrieval based on corel dataset using deep learning

Content-based image retrieval based on corel dataset using deep learning

Content based image retrieval by ensembles of deep learning object classifiers

Deep learning for fast bronze inscription image retrieval

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