Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automated scene analysis can classify images into semantically meaningful categories and can be achieved by various means [1][2]. In this study,  have used spatial envelope to extract features from 529 images of 3 semantic scene categories i.e. indoor, landscape and outdoor scenes. Spatial envelope was represented by a Gist descriptor, resulting in 256 features per image. A Principle component analysis was used to reduce dimensionality of feature space from 256 dimensions to 25, persevering 90% of the variance in feature space. This reduced dimensional dataset was subjected to unsupervised learning method k-means clustering. Our results show 82% accuracy in scene categorization with few images assigned to wrong clusters, possibly due to the similarity in spatial envelope. Furthermore,  accessed quality of found clusters using isolation distance metric.
['Syed Omer Gilani', 'Mohsin Jamil', 'Zahra Fazal', 'Muhammad Naveed', 'Rabiel Sakina']
I am writing a research paper on a idea Automated Scene Analysis by Image Feature Extraction
[{"from": "human", "value": "I am writing a research paper on a idea Automated Scene Analysis by Image Feature Extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automated scene analysis can classify images into semantically meaningful categories and can be achieved by various means [1][2]. In this study,  have used spatial envelope to extract features from 529 images of 3 semantic scene categories i.e. indoor, landscape and outdoor scenes. Spatial envelope was represented by a Gist descriptor, resulting in 256 features per image. A Principle component analysis was used to reduce dimensionality of feature space from 256 dimensions to 25, persevering 90% of the variance in feature space. This reduced dimensional dataset was subjected to unsupervised learning method k-means clustering. Our results show 82% accuracy in scene categorization with few images assigned to wrong clusters, possibly due to the similarity in spatial envelope. Furthermore,  accessed quality of found clusters using isolation distance metric."}]