![]() Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object- based classification. For example, we find that supervised object- based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object- based framework. Third, useful data on supervised object- based image classification are determined from the meta-analysis. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object- based land-cover classification processes. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. Object- based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Ma, Lei Li, Manchun Ma, Xiaoxue Cheng, Liang Du, Peijun Liu, Yongxue A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.Ī review of supervised object- based land-cover image classification Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. This paper proposes a general framework for iris image classification based on texture analysis. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection ( classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification ( classification of all iris images in the central database into multiple categories). Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. Zhenan Sun Hui Zhang Tieniu Tan Jianyu Wang Iris Image Classification Based on Hierarchical Visual Codebook.
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