Image classification and retrieval using optimized Pulse-Coupled Neural Network. Issue 11 (1st July 2015)

• Record Type: Journal Article
• Authors: Mohammed, Mona Mahrous; Badr, Amr; Abdelhalim, M.B.
• Is Part Of: Expert systems with applications. Volume 42:Issue 11(2015)
• Page Start: 4927
• Page End: 4936
• Publication Date: 2015-07-01
• Subjects: Content-Based Image Retrieval (CBIR) -- Image classification -- Visual features -- Pulse-Coupled Neural Network (PCNN) -- Image signature -- K-Nearest Neighbor -- Genetic algorithm; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.02.019
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose an image classification and retrieval technique using PCNN and K-NN. We optimized the PCNN parameters using genetic algorithm. We implemented a prototype to validate our proposed technique. The results are represented and measured with precision, recall and accuracy. The proposed technique proved its efficiency in classifying and retrieving images with comparison to other techniques. Abstract: Content-Based Image Retrieval (CBIR) has become a powerful tool that is used in many image applications and search engines. Thus, many techniques and approaches for CBIR were developed in literature. The CBIR approach works on the visual features of the image rather than a descriptive text. Therefore, it provides more effective and efficient retrieval. On the other hand, PCNN has proved its efficiency as an image processing tool for various tasks such as image segmentation and recognition, feature extraction, edge and object detection. This article introduces a technique for content-based image classification and retrieval using PCNN. The proposed technique uses an optimized Pulse-Coupled Neural Network (PCNN) to extract the visual features of the image in a form of a numeric vector called image signature. An optimization mechanism was applied to the PCNN parameters in order to improve the signature quality. Thus improving the classification and retrieval results. Additionally, it employs the K-Nearest Neighbor (K-NN) algorithm for classification and matching. By applying classification before retrieval, the number of images in the search space is optimized to include one category instead of multiple categories. Moreover, we developed a CBIR prototype to validate our technique. The results show that our technique can retrieve and classify images efficiently. Furthermore, we evaluated our prototype against one of the widely used techniques and it was proven that the proposed technique can enhance the search results and improve the accuracy by 3.5%.