Image Retrieval using Generalized Gaussian Distribution and Score based Support Vector Machine

Objectives: Retrieving images from huge volumes of image database has its application in broad areas like medicine, agriculture, military etc. Annotation based approaches have become obsolete because they are time consuming and cannot describe the image effectively. The rich content in the images can overcome the limitations of annotation based techniques. Texture is the most vital visual cue used to analyze images. Methods: In the proposed technique, the image texture features are statistically represented using Generalized Gaussian Distribution in the wavelet domain. A linear score based Support Vector Machine is incorporated to identify analogous patterns to the query image from the database. Findings: The efficacy of the proposed algorithm is ascertained by conducting extensive experiments. Two texture image database of size 1400 and 1920 is used for our experiment. The proposed algorithm is verified in terms of average recall performance against the standard benchmark algorithms. It is observed that the proposed score based SVM yields higher precision and flexibility in separating the similarity within the classes and dissimilarity across different classes. Improvements/Applications: Compared to the traditional approaches, the retrieval rate of this method is improved by 30% at a considerably low computational complexity.