Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Previous work has shown Gaussian mixture vector quantization (GMVQ) based classifiers to be effective in classifying image blocks, image regions and whole images. A significant attraction of GMVQ for whole image classification is that simple local features can be used, thereby avoiding time-consuming feature design and selection. Unfortunately, hover, this approach does not work so ll when the artifact of interest occupies a small area relative to the size of the image.  propose a simple ighting approach to focus the classifier's attention on the artifact blocks. This extends the usefulness of whole image GMVQ classification without compromising the simplicity of feature selection. The algorithm is motivated by difficulties in classifying pipeline images. Results on this dataset show the ighted GMVQ approach to be effective for classifying images with small artifacts of interest.
['David O’Brien', 'Robert M. Gray']
I am writing a research paper on a idea Gaussian mixture model classifiers for small objects in images
[{"from": "human", "value": "I am writing a research paper on a idea Gaussian mixture model classifiers for small objects in images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Previous work has shown Gaussian mixture vector quantization (GMVQ) based classifiers to be effective in classifying image blocks, image regions and whole images. A significant attraction of GMVQ for whole image classification is that simple local features can be used, thereby avoiding time-consuming feature design and selection. Unfortunately, hover, this approach does not work so ll when the artifact of interest occupies a small area relative to the size of the image.  propose a simple ighting approach to focus the classifier's attention on the artifact blocks. This extends the usefulness of whole image GMVQ classification without compromising the simplicity of feature selection. The algorithm is motivated by difficulties in classifying pipeline images. Results on this dataset show the ighted GMVQ approach to be effective for classifying images with small artifacts of interest."}]