Improving Image Recognition Accuracy Using Multi-Views Spectral Clustering

This paper presents a new way to tremendously improve the picture clustering quality by exploiting multiple "views" of the data.Image grouping is a process of grouping photographs associated with visual characteristics.The most appropriate characteristics and AI architectures for picture clustering have, to date, been very difficult to select although they have a significant impact on the quality of the clustering results.As a solution to the challenge, the so-called Multi-View Clustering (MVC) is proposed.In MVC, multiple AI networks, which are already pre-trained, like convolutional neural networks (CNNs), act as multiple "views" with regards to the same visual information.While drawing from the same data, each of these CNNs captures another perspective by extracting a unique set of image features.This method will attempt to consider various points of view in order to collect different and complementary information about the images.A neural network architecture with multiple inputs is proposed for this many-views problem.Trained end-to-end, this resolves the MVC problem using the features extracted from each of the CNN views as input.Improved pooling performance is a result of end-to-end training that ensures the network has learnt how to aggregate features from multiple views efficiently.Experimental results on several image datasets have proven the usefulness of this strategy.The proposed method is with an end-to-end training strategy, utilizing several jointly pre-trained CNNs as feature extractors, so it outperforms conventional image clustering accuracy.Indeed, state-of-the-art results are produced in the field of image collage.Conclusively, this paper proposes a holistic approach that improves the efficiency of image classification, which is a critical contribution to the literature on image clustering.The approach proposed overcomes the challenge of feature selection and AI architecture for the image clustering through the use of multiple views of spectral ensembles to some pre-trained AI networks and leveraging an end-to-end training approach.The findings show how the efficiency of picture grouping methods is improved through the incorporation of numerous viewpoints.