Adaptive processing of data structures for image content classification, indexing and retrieval of flowers

Abstract

Computer-aided flower identification is a very useful tool for plant species identification aspect. In this research, a study was made on a development of machine learning system to characterize flower images efficiently. One of the most popular frameworks for the adaptive processing of data structures to date, was proposed by Frasconi et al., who used a Backpropagation Through Structures (BPTS) algorithm to carry out supervised learning. This supervised model has been successfully applied to a number of learning tasks that involve complex symbolic structural patterns, such as image semantic structures, internet behavior, and chemical compounds. In this project, we extend this model, using probabilistic estimates to acquire discriminative information from the learning patterns. Using this probabilistic estimation, smooth discriminant boundaries can be obtained through a process of clustering onto the observed input attributes. This approach enhances the ability of class discrimination techniques to recognize structural patterns. The capabilities of the proposed model are evaluated by the flowers image classification. The obtained results significantly support the capabilities of our proposed approach to classify and recognize flowers in terms of generalization and noise robustness.