Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler problems, allowing the application of numerous well-established learning algorithms that may not apply directly to the original task. This work focuses on the efficiency of decomposition-based methods and proposes several improvements to the meta-learning level. In this paper, four methods for optimizing the ensemble phase of multiclass classification are introduced. The first demonstrates that employing a mixture of experts scheme can drastically reduce the number of operations in the training phase by eliminating redundant learning processes in decomposition-based techniques for multiclass problems. The second technique for combining learner-based outcomes relies on Bayes’ theorem. Combining the Bayes rule with arbitrary decompositions reduces training complexity relative to the number of classifiers even further. Two additional methods are also proposed for increasing the final classification accuracy by decomposing the initial task into smaller ones and ensembling the output of the base learners along with that of a multiclass classifier. Finally, the proposed novel meta-learning techniques are evaluated on four distinct datasets of varying classification difficulty. In every case, the proposed methods present a substantial accuracy improvement over existing traditional image classification techniques.

Urban waste management is a most challenging issue for modern societies. Reducing pollution and saving environmental resources provides significant opportunities for local, national and international economic growth. In Greece, the recycling rates are currently low compared to other European countries. The current study proposes an autonomous, intelligent robotic system for categorizing and separating recyclable materials aiming to contribute in increasing the recycling rates in Greece. The system is a series connection of an optical sub-system and a robotic sub-system. The optical subsystem receives input from a ordinary RGB and an NIR camera. These are processed in combination for the identification and categorization of recyclables into predefined material types. The output of the optical sub-system provides a list of potential targets (recyclables) to be picked and sorted. This is forwarded to the robotic subsystem, which undertakes the physical separation of the materials to the appropriate bin. The proposed system, named ANASA system, has been deployed in two different urban waste management industrial units, in DEDISA, Chania, Crete, Greece (processing recyclable wastes) and in ESDAK, Heraklion, Crete, Greece (processing composite wastes), where the system's reliability and validity is experimentally tested in real industrial environments. The advantages over the existing ordinary recycling systems are significant: high reliability in object recognition (material detection), short separation cycle (high speed), significantly low installation volume, low cost and ease of application to both old and new recycling industries. The combination of the above features provides a potential for exploitation as a complete commercial commercialization.