Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an annotation tool developed by the DISA Laboratory for the ImageCLEF 2014 Scalable Concept Image An- notation challenge. Our solution exploits the search-based annotation paradigm and utilizes several sources of semantic information to deter- mine the relevance of candidate concepts. Rather than relying on the quality of training data, our approach profits from the large quantities of information available in large image collections and semantic knowledge bases. The results achieved by our system confirm that this approach is very promising for scalable image annotation. While modern technologies allow people to create and store data in many forms (e.g. images, video, etc.), the most natural way of expressing one's need for a specific piece of data is still a text query. Natural language remains the primary means of information transfer both for person-to-person communication and person-to-computer interactions. Hover, a lot of existing digital data is not associated with any text information that would help users access or categorize the data. The purpose of automatic annotation is to increase the findability of information by bridging the gap beten data and query representations. Since 2006, the ImageCLEF initiative has been encouraging the develop- ment of image annotation tools by organizing competitions on automatic image classification and annotation. In the ImageCLEF 2014 Scalable Concept Image Annotation challenge, participants re required to develop solutions that can annotate common personal images using only automatically obtained training data. Utilization of manually prepared training samples was forbidden to ensure that the solutions would easily scale to larger concept sets.  presents the annotation tool developed for this task by the DISA Laboratory 1 at Masaryk University. Our solution exploits the search-based an- notation paradigm and utilizes several sources of semantic information to deter- mine the probability of candidate concepts. Rather than relying on the quality of training data, our approach profits from the large quantities of information available in large image collections and semantic knowledge bases. The results achieved by our system confirm the strengths of this approach. 1 http://disa.fi.muni.cz
['Petra Budikova', 'Jan Botorek', 'Michal Batko', 'Pavel Zezula']
I am writing a research paper on a idea DISA at ImageCLEF 2014: The search-based solution for scalable image annotation
[{"from": "human", "value": "I am writing a research paper on a idea DISA at ImageCLEF 2014: The search-based solution for scalable image annotation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an annotation tool developed by the DISA Laboratory for the ImageCLEF 2014 Scalable Concept Image An- notation challenge. Our solution exploits the search-based annotation paradigm and utilizes several sources of semantic information to deter- mine the relevance of candidate concepts. Rather than relying on the quality of training data, our approach profits from the large quantities of information available in large image collections and semantic knowledge bases. The results achieved by our system confirm that this approach is very promising for scalable image annotation. While modern technologies allow people to create and store data in many forms (e.g. images, video, etc.), the most natural way of expressing one's need for a specific piece of data is still a text query. Natural language remains the primary means of information transfer both for person-to-person communication and person-to-computer interactions. Hover, a lot of existing digital data is not associated with any text information that would help users access or categorize the data. The purpose of automatic annotation is to increase the findability of information by bridging the gap beten data and query representations. Since 2006, the ImageCLEF initiative has been encouraging the develop- ment of image annotation tools by organizing competitions on automatic image classification and annotation. In the ImageCLEF 2014 Scalable Concept Image Annotation challenge, participants re required to develop solutions that can annotate common personal images using only automatically obtained training data. Utilization of manually prepared training samples was forbidden to ensure that the solutions would easily scale to larger concept sets.  presents the annotation tool developed for this task by the DISA Laboratory 1 at Masaryk University. Our solution exploits the search-based an- notation paradigm and utilizes several sources of semantic information to deter- mine the probability of candidate concepts. Rather than relying on the quality of training data, our approach profits from the large quantities of information available in large image collections and semantic knowledge bases. The results achieved by our system confirm the strengths of this approach. 1 http://disa.fi.muni.cz"}]