Develop and implement unsupervised learning through hybrid FFPA clustering in large-scale datasets

Clustering is extensively realistic and considered in computer vision that follows unsupervised learning principles. In this, the performance of a clustering process mainly depends on the feature representation. Generally, the clustering process may have an error rate, and this affects the feature representation. To avoid this, unsupervised Learning (USL) provides an alternative path to obtain the best clusters from the dataset through the optimum features. In this proposed work, the clustering process is done by using a hybrid firefly-based flower pollination algorithm (FFPA). So this clustering process removes the complexity in USL. The better performance is obtained by identifying an essential group from the data to avoid the problems obtained by a USL. In the standard USL, the PCA method is used to minimize a large amount of original data. Here, the features are extracted based on RGB features and Zernike moments, and this is given to the input for the hybrid cluster. Finally, the hybrid convolutional neural network classifier, along with the datasets that are trained from a similar patch manifold, is used to create a label for several datasets. The performance of this proposed method portrays that the local features are effectively clustered from the various datasets by an unsupervised FFPA algorithm. In this work, the unsupervised clustering process with a hybrid classification for the object recognition application is used. In this work, the average accuracy, error rate, and run time are nearly 95%, 73%, and 26 s, respectively.