Image Classification of Multimedia Platforms With Texture Algorithms and Neural Networks - Mauricio Sauzameda-Gonzalez; Josue Alvarez-Borrego; Esperanza Guerra-Rosas (IEEE Access)
In this paper, a methodology for real-time image classification on multimedia platforms has been developed. For this purpose, six feedforward neural network models were trained with images from two databases, which were preprocessed by three texture extraction methods: local binary pattern-uniform (LBP-U), gray level co-occurrence matrix (GLCM), and wavelet image scattering (WIS). The databases used consist of 157,448 images of the sections with the thumbnails of the platform content (mosaics) representing 14 classes and 38,214 images with the descriptions of the available content (descriptors) representing 11 classes, where all images have a resolution of $1280× 720$ pixels. The six models (three for mosaics and three for descriptors) were validated with images from the databases, which were not part of the training process, to obtain their performance metrics. The training and validation process was performed 30 times, and the average results were compared. The most outstanding models for each database were the neural networks trained with the wavelet image scattering method, with metrics of $99.97± 0.01$ % accuracy, $99.99± 0.01$ % specificity, $99.84± 0.06$ % sensitivity, $99.59± 0.13$ % precision and $99.71± 0.~08$ % of F1 score with a response time of 0.7349 seconds for the model trained with mosaics and with metrics of $99.90± 0.03$ % of accuracy, $99.94± 0.02$ % of specificity, $99.58± 0.15$ % of sensitivity, $98.63± 0.55$ % of precision and $99.09± 0.30$ % of F1 score with a response time of 0.6227 seconds for the model trained with descriptor images. The results are very significant due to the high efficiency obtained and confirm the effectiveness of the models with the WIS method for the classification of multimedia platform images with the characteristics of the databases used. It is suggested that the remaining methods be adjusted to improve their performance.