Cubic-splines neural network- based system for Image Retrieval

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.