Abstract
The usual procedure used in Content Based Image retrieval (CBIR), is to extract some useful low-level features such as color, texture and shape from the query image and retrieve images that have a similar set of features. However, the problem with using low-level features is the semantic gap between image feature representation and human visual understanding. That is why many researchers are devoted for improving content-based image retrieval methods with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Those researchers are mainly focused on combining low-level features together to have a better representation of the content of an image, which make it closer to the human visual perception but still not close enough to reduce the semantic gap. In this paper we’ll start by a comprehensive review on the recent researches in the field of Image Retrieval, then we propose a CBIR system based on convolutional neural network and transfer learning to extract high-level features, as an initiative part of a larger project that aims to retrieve and collect images containing the Arabic language for natural language processing tasks.
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Notes
- 1.
HSV: Hue, Saturation, Value.
- 2.
GLCM: Gray-Level Co-occurrence Matrix.
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Jabnoun, J., Haffar, N., Zrigui, A., Nsir, S., Nicolas, H., Trigui, A. (2022). An Image Retrieval System Using Deep Learning to Extract High-Level Features. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_13
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