Human at every age, can recognize any kind of object very easily even at a short time just by observing it. Recognizing an object has been a challenge for a machine since the introduction of a computation model that mimics how brain’s neurons work more than 70 years ago. The invention of artificial neural network especially its derivative namely deep learning has improved the performance of an intelligent machine’s object recognition. However, the training scheme that is a part of neural network-based learning methodology requires other efforts such as providing a huge number of data along with their annotation that impacts to the need of high-performance computing equipment. Faced to the need of an object recognizer that requires just a small number of information, light computing, and can be deployed quickly without the hassle of doing any training, we propose a fast object recognizer inspired by human cognitive computation called as Knowledge Growing System (KGS) which is a model of Cognitive Artificial Intelligence. By using the Iris dataset, the one that has been proven as test data for years, we proved that KGS can obtain an accuracy of 85.93 % in average by only observing a small number of information, that is only 15 data out of 150 or 10%. Based on this result, we plan to extend KGS to recognize more complex objects such as airplanes and unmanned vehicles.
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