Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance.  proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as ll as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.
['Miguel Oliveira', 'Luís Seabra Lopes', 'Gi Hyun Lim', 'S. Hamidreza Kasaei', 'Angel Domingo Sappa', 'Ana Maria Tomé']
I am writing a research paper on a idea Concurrent learning of visual codebooks and object categories in open-ended domains
[{"from": "human", "value": "I am writing a research paper on a idea Concurrent learning of visual codebooks and object categories in open-ended domains"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance.  proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as ll as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline."}]