A hybrid codebook model for object categorization using two-way clustering based codebook generation method. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Chebbout, Samira; Merouani, Hayet Farida
• Is Part Of: International journal of computers and applications. Volume 44:Issue 2(2022)
• Page Start: 178
• Page End: 186
• Publication Date: 2022-02-01
• Subjects: Computer vision -- object categorization -- machine learning -- visual codebook -- simultaneous clustering; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2020.1712775
• Languages: English
• ISSNs: 1206-212X

Abstract

Abstract : Both the visual codebook and the codebook model are considered as two main parts of most object classification frameworks. In the original codebook model, each image descriptor is encoded using a single codebook obtained usually using a clustering approach. In this paper, we propose a hybrid codebook model for an object classification task. A simultaneous clustering approach is applied to image descriptors to generate two variant codebooks and used them separately to encode and represent an image through a patch-based codebook model and a feature-based codebook model respectively. The proposed codebook model has been tested on the Caltech-101 dataset. Experimental results demonstrate state-of-the-art performance compared to typical clustering-based codebook model.