We resort to the online learning method to optimize the dictionary which then served as a codebook for spare coding of multi-features. Finally, we facilitate the linear support vector machine to classify images. The experimental results on two painting image datasets show that, compared with the ...
However only recently the focus has shifted to exploiting low-level features to classify images automatically into semantically meaningful and broad categories. This paper presents a novel automatic color image classification algorithm. Initially the color image is divided into classes; each class is a ...
A finite set of unambiguous instructions that, given some set of initial conditions, can be performed in a prescribed sequence to achieve a certain goal and that has a recognizable set of end conditions. [Variant (probably influenced byarithmetic) ofalgorism.] ...
In January 2021, OpenAI presented a 12-billion parameter network calledDALL·Etrained to generate images from text descriptions, using a large dataset of text-image pairs [1]. While there have been many attempts to createtext-to-image synthesis systems[112, 135], a turning point regarding this...
Then, the selected features are given to the support vector machine (SVM) classifier to classify an image as normal or abnormal. After the classification process, the abnormal images are selected and given to the segmentation process. For segmentation, in this paper, we utilized an effective ...
network to classify WBCs in BCCD dataset. A new complicated fused CNN introduced in26was trained from scratch on 10,253 augmented WBCs images from the BCCD dataset. Despite the complexity of the proposed CNN in26, the number of its parameters stands at 133,000....
(2022). This study utilized a range of machine learning and deep learning algorithms to classify the water status of winter wheat based on RGB and thermal images. The experiment defined four irrigation water gradients: 100 %, 75 %, 50 %, and 25 % of crop evapotranspiration. The ...
aGenomic is now a national company Genomic现在是一家全国公司[translate] aprincipal component analysis (PCA) algorithm to choose the most effective components of traffc sign images to classify an 主要成分选择traffc标志图象最有效的组分的分析(PCA)算法分类[translate]...
InHong, Gao, et al. (2020), authors proposed Graph Convolution Networks (GCNs) to classifyhyperspectral images. GCNs are a type of neural network that can operate directly on graphs, which makes them well-suited for working with hyperspectral data, as each pixel in a hyperspectral image can ...
Berke and colleagues23,24 used it to distinguish infected and healthy parts of potato tubers, to classify chip samples and to perform classification tasks on multi- and hyperspectral space images, highlighting its applicability to aerial and space images with more than 20 independent spectral bands....