Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image retrieval over a network is the focus of this paper, as being a major challenge of content based image retrieval.  present a system that gathers feedbacks given by the users in order to learn the location of the searched images. As a result, the active learning of a content based relevance function is enhanced by the selection of hosts containing relevant examples.  achieve a long term merging of feedbacks over many sessions, without any knowledge about the category being searched at any session. Our system is based on mobile agents crawling the network in an ant-like behavior. Markers are used to learn a routing of the agent leading them to the relevant images. The benefits of the ant-like agents system is a natural parallelization of the processing as ll as a distributed approach to the routing learning.  made experiments on the trecvidpsila05 key-frame dataset showing that the location of the categories re efficiently learned. Furthermore, the long-term learning of categories improves the interaction by reducing the number of labels needed during the interaction to obtain satisfying results.
['David Picard', 'Arnaud Revel', 'Matthieu Cord']
I am writing a research paper on a idea Image retrieval over networks: Ant algorithm for long term active learning
[{"from": "human", "value": "I am writing a research paper on a idea Image retrieval over networks: Ant algorithm for long term active learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image retrieval over a network is the focus of this paper, as being a major challenge of content based image retrieval.  present a system that gathers feedbacks given by the users in order to learn the location of the searched images. As a result, the active learning of a content based relevance function is enhanced by the selection of hosts containing relevant examples.  achieve a long term merging of feedbacks over many sessions, without any knowledge about the category being searched at any session. Our system is based on mobile agents crawling the network in an ant-like behavior. Markers are used to learn a routing of the agent leading them to the relevant images. The benefits of the ant-like agents system is a natural parallelization of the processing as ll as a distributed approach to the routing learning.  made experiments on the trecvidpsila05 key-frame dataset showing that the location of the categories re efficiently learned. Furthermore, the long-term learning of categories improves the interaction by reducing the number of labels needed during the interaction to obtain satisfying results."}]