Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence beten visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. Hover, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal ights to these dimensions, and become confounded in the process of dealing with these dimensions. ,  propose a top-down, subspace clustering algorithm as a solution to this problem. For a given cluster,  determine relevant features based on histogram analysis and assign greater ight to relevant features as compared to less relevant features.  have implemented four different models to link visual tokens with keywords based on the clustering results of our clustering algorithm and K-means algorithm, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that our algorithm is better than traditional ones for automatic image annotation and retrieval.
['Lei Wang', 'Li Liu', 'Latifur Khan']
I am writing a research paper on a idea Automatic image annotation and retrieval using subspace clustering algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Automatic image annotation and retrieval using subspace clustering algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence beten visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. Hover, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal ights to these dimensions, and become confounded in the process of dealing with these dimensions. ,  propose a top-down, subspace clustering algorithm as a solution to this problem. For a given cluster,  determine relevant features based on histogram analysis and assign greater ight to relevant features as compared to less relevant features.  have implemented four different models to link visual tokens with keywords based on the clustering results of our clustering algorithm and K-means algorithm, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that our algorithm is better than traditional ones for automatic image annotation and retrieval."}]