A Random Vector Functional Link Network Based Content Based Image Retrieval

Image Retrieval (IR) framework enables the user to search query images in order to retrieve images stored in the database according to their advantage. Content Based Image Retrieval (CBIR) is a technique which uses visual features of an image such as color, shape and texture feature to retrieve images of user interest. The major issue that exists in any CBIR system is semantic gap and computational time. Hence this work aims to provide an exchange off between computational time and accuracy. To extract the color, texture and shape feature, the RGB color histogram from the three independent color channels, Local Binary Pattern (LBP) from the gray scale image Histogram of oriented gradients are derived respectively. These three features are concatenated to obtain the feature vector of the images in the database. The dimensionality of the feature vector is reduced by Linear Discriminant Analysis (LDA). The compact feature vector set is trained using Random Vector Functional Link (RVFL) network to create the knowledge base. In the testing phase, once the user rises a query, the query feature vector is derived for the corresponding query image and tested against the knowledge base using RVFL classifier. Using the class code obtained by RVFL classifier, the images are retrieved using Minkowski distance. The performance of the proposed algorithm is validated by evaluating it on a Corel Image database using metrics like precision, Recall and F-score. The proposed feature combination along with LDA and RVFL provides better results in retrieving the query image.