Intrinsic structure based feature transform for image classification

• Published in: Journal of visual communication and image representation 2016-07, Vol.38, p.735-744
• Main Authors: Zhou, Zhengjuan, Waqas, Jadoon
• Format: Article
• Language: English
• Subjects: Construction; Data points; Dimensionality reduction; Euclidean geometry; Graphs; Image classification; Manifold learning; Mathematical analysis; Reduction; Subspace learning; Transforms
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2016.04.016

•A new dimensionality reduction method called ISFT is proposed.•ISFT captures the local and global structure of data simultaneously.•ISFT captures the local structure using sets as input rather than single image.•ISFT captures the global structure using representative images for each class. Most dimensionality reduction works construct the nearest-neighbor graph by using Euclidean distance between images; this type of distance may not reflect the intrinsic structure. Different from existing methods, we propose to use sets as input rather than single images for accurate distance calculation. The set named as neighbor circle consists of the corresponding data point and its neighbors in the same class. Then a supervised dimensionality reduction method is developed, i.e., intrinsic structure feature transform (ISFT), it captures the local structure by constructing the nearest-neighbor graph using the Log-Euclidean distance as measurements of neighbor circles. Furthermore, ISFT finds representative images for each class; it captures the global structure by using the projected samples of these representatives to maximize the between-class scatter measure. The proposed method is compared with several state-of-the-art dimensionality reduction methods on various publicly available databases. Extensive experimental results have demonstrated the effectiveness of the proposed approach.