N-Gram Based Image Representation and Classification Using Perceptual Shape Features

Abstract

Rapid growth of visual data processing and analysis applications, such as content based image retrieval, augmented reality, automated inspection and defect detection, medical image understanding, and remote sensing has made the problem of developing accurate and efficient image representation and classification methods one of the key research areas. This research proposes new higher-level perceptual shape features for image representation which are based on Gestalt principles of human vision. The concept of n-gram is adapted from text analysis as a grouping mechanism for coding global shape content of an image. The proposed perceptual shape features are translation, rotation, and scale invariant. Local shape features and n-gram grouping scheme are integrated together to create new Perceptual Shape Vocabulary (PSV). Different image representations based on PSVs with and without n-gram scheme are applied to image classification task using Support Vector Machine (SVM) classifier. The experimental evaluation results indicate that n-gram-based perceptual shape features can efficiently represent global shape information of an image, and augment the accuracy of image representation by low-level image features such as SIFT descriptors.