Version 1
: Received: 5 October 2022 / Approved: 7 October 2022 / Online: 7 October 2022 (14:58:09 CEST)
How to cite:
Alsaghir, E.; Shi, X.; De Silva, V. A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning. Preprints2022, 2022100081. https://doi.org/10.20944/preprints202210.0081.v1
Alsaghir, E.; Shi, X.; De Silva, V. A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning. Preprints 2022, 2022100081. https://doi.org/10.20944/preprints202210.0081.v1
Alsaghir, E.; Shi, X.; De Silva, V. A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning. Preprints2022, 2022100081. https://doi.org/10.20944/preprints202210.0081.v1
APA Style
Alsaghir, E., Shi, X., & De Silva, V. (2022). A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning. Preprints. https://doi.org/10.20944/preprints202210.0081.v1
Chicago/Turabian Style
Alsaghir, E., Xiyu Shi and Varuna De Silva. 2022 "A Model Selection Algorithm for Complex Multi-Domain Cnn Systems Based on Feature-Weights Relation in Deep Learning" Preprints. https://doi.org/10.20944/preprints202210.0081.v1
Abstract
Object recognition is an essential element of machine intelligence tasks. However, one model cannot practically be trained to identify all the possible objects it encounters. An ensemble of models may be needed to cater to a broader range of objects. Building a mathematical understanding of the relationship between various objects that share comparable outlined features is envisaged as an effective method of improving the model ensemble through a pre-processing stage, where these objects' features are grouped under a broader classification umbrella. This paper proposes a mechanism to train an ensemble of recognition models coupled with a model selection scheme to scale-up object recognition in a multi-model system. An algorithmic relationship between the learnt parameters of a trained classification model and the features of input images is presented in the paper for the system to learn the model selection scheme. The multiple models are built with a CNN structure, whereas the image features are extracted using a CNN/VGG16 architecture. Based on the models' excitation weights, a neural network model selection algorithm, which links a new object with the models and decides how close the features of the object are to the trained models for selecting a particular model for object recognition is developed and tested on a five-model neural network platform. The experiment results show the proposed model selection scheme is highly effective and accurate in selecting an appropriate model for a network of multiple models.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
