Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image auto-annotation is a challenging task in computer vision. The goal of this task is to predict multiple words for generic images automatically. Recent state-of-theart methods are based on a non-parametric approach that uses several visual features to calculate distances beten image samples. While this approach is successful from the viewpoint of annotation accuracy, the computational costs, in terms of both complexity and memory use, tend to be high, since non-parametric methods require many training instances to be stored in memory to compute distances from a query. ,  investigate several linear dimensionality reduction methods for efficient image annotation. Using the additional information provided by multiple labels,  can obtain a small representation preserving (and hopefully improving) the semantic distance of a visual feature. Linear methods are computationally reasonable and are suitable for practical large-scale systems, although only limited comparison of such methods is available in this research field. Extensive experiments and analyses on various datasets and visual features show how these simple methods can be applied effectively to image annotation.
['Hideki Nakayama', 'Tatsuya Harada', 'Yasuo Kuniyoshi']
I am writing a research paper on a idea Evaluation of dimensionality reduction methods for image auto-annotation
[{"from": "human", "value": "I am writing a research paper on a idea Evaluation of dimensionality reduction methods for image auto-annotation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image auto-annotation is a challenging task in computer vision. The goal of this task is to predict multiple words for generic images automatically. Recent state-of-theart methods are based on a non-parametric approach that uses several visual features to calculate distances beten image samples. While this approach is successful from the viewpoint of annotation accuracy, the computational costs, in terms of both complexity and memory use, tend to be high, since non-parametric methods require many training instances to be stored in memory to compute distances from a query. ,  investigate several linear dimensionality reduction methods for efficient image annotation. Using the additional information provided by multiple labels,  can obtain a small representation preserving (and hopefully improving) the semantic distance of a visual feature. Linear methods are computationally reasonable and are suitable for practical large-scale systems, although only limited comparison of such methods is available in this research field. Extensive experiments and analyses on various datasets and visual features show how these simple methods can be applied effectively to image annotation."}]