title: Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?
abstract: Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.
authors: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China ; Key Laboratory of Optic-Electronic and Communication, Jiangxi Science & Technology Normal University, Nanchang 330013, China ; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
authors: Fan
authors: 0000-0002-4324-1088
authors: F
authors: Yang
authors: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China ; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
authors: Ying-Ying
authors: YY
authors: Xu
authors: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China ; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
authors: Hong-Bin
authors: HB
authors: Shen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007150:Immunohistochemistry; D042541:Intracellular Space / Q000378:metabolism; D008856:Microscopy, Fluorescence; D010363:Pattern Recognition, Automated; D021381:Protein Transport; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins