@inproceedings{6f9961d4d0304a5e88e8840de406da57,
title = "Cubic-splines neural network-based system for image retrieval",
abstract = "Research in Content-Based Image Retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named SNNIR (Splines Neural Network-based Image Retrieval). SNNIR system makes use of a rapid and precise neural model. This model employs a cubic-splines activation function. By using the spline neural model, the gap between the low-level visual features and the high-level concepts is minimized. Experimental results show that the proposed system achieves high accuracy and effectiveness in terms of precision and recall compared with other CBIR systems.",
keywords = "Content-based retrieval, Cubic-splines neural network, Feature extraction",
author = "Samy Sadek and Ayoub Al-Hamadi and Bernd Michaelis and Usama Sayed",
year = "2009",
doi = "10.1109/ICIP.2009.5413561",
language = "English",
isbn = "9781424456543",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "273--276",
booktitle = "2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings",
address = "United States",
note = "2009 IEEE International Conference on Image Processing, ICIP 2009 ; Conference date: 07-11-2009 Through 10-11-2009",
}