Quaternionic extended local binary pattern with adaptive structural pyramid pooling for color image representation. (July 2021)

• Record Type: Journal Article
• Authors: Song, Tiecheng; Xin, Liangliang; Gao, Chenqiang; Zhang, Tianqi; Huang, Yao
• Is Part Of: Pattern recognition. Volume 115(2021)
• Publication Date: 2021-07
• Subjects: Color image -- Quaternion -- Local binary pattern -- Spatial pooling -- Image classification -- Scene categorization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107891
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a quaternionic extended local binary pattern for color image representation. We propose an extended quaternionic representation by introducing an information term into the real part of a quaternion. We propose adaptive structural pyramid pooling to aggregate encoded features into histogram representations. Our method achieves the state-of-the-art results for color texture classification and scene categorization. Abstract: This paper proposes a novel feature representation method for color images, namely quaternionic extended local binary pattern (QxLBP) with adaptive structural pyramid pooling (ASPP). First, we propose a QxLBP operator to encode local neighboring information and complementary modulus and phase information in the quaternion domain of color images. In QxLBP, an extended quaternionic representation (EQR) is proposed which introduces an information term into the real part of a quaternion. The resulting EQR enables us to flexibly encode discriminative features and handle multichannel image data. Second, we propose ASPP as a multiresolution pooling way to aggregate local features. Unlike the traditional spatial pyramid pooling which is sensitive to image rotation and spatial changes, ASPP is structure-oriented pooling which can adaptively aggregate the encoded features into multiresolution histogram representations. Experiments on four benchmark image datasets demonstrate that the proposed method achieves the state-of-the-art performance for color texture classification and scene categorization.