Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a hierarchical-grid (HG) feature analysis framework for representing images in automatic image annotation (AIA).  explore the properties of codebooks constructed with different-sized grids in image sub-blocks, and co-occurrence relationship beten VQ codewords constructed from different grid systems. The proposed HG approach is evaluated on the TRECVID 2005 data set using classifiers obtained with maximal figure-of-merit discriminative training. With multi-level and cross-level grid systems incorporating bigram information within and beten higher and lor grid levels,  show that the AIA performance can be significantly improved. For 20 selected concepts from the 39-concept LSCOM-Lite annotation set,  achieve a best F 1  in almost all the concepts. The overall performance improvement with the combined multi-level and cross-level grid systems over the best single-size grid system in micro F 1  is about 12.1%.
['Ilseo Kim', 'Chin-Hui Lee']
I am writing a research paper on a idea A hierarchical grid feature representation framework for automatic image annotation
[{"from": "human", "value": "I am writing a research paper on a idea A hierarchical grid feature representation framework for automatic 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:  propose a hierarchical-grid (HG) feature analysis framework for representing images in automatic image annotation (AIA).  explore the properties of codebooks constructed with different-sized grids in image sub-blocks, and co-occurrence relationship beten VQ codewords constructed from different grid systems. The proposed HG approach is evaluated on the TRECVID 2005 data set using classifiers obtained with maximal figure-of-merit discriminative training. With multi-level and cross-level grid systems incorporating bigram information within and beten higher and lor grid levels,  show that the AIA performance can be significantly improved. For 20 selected concepts from the 39-concept LSCOM-Lite annotation set,  achieve a best F 1  in almost all the concepts. The overall performance improvement with the combined multi-level and cross-level grid systems over the best single-size grid system in micro F 1  is about 12.1%."}]