Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Wireless capsule endoscopy (WCE) needs computerized method to reduce the review time for its large image data. ,  propose an improved bag of feature (BoF) method to assist classification of polyps in WCE images. Instead of utilizing a single scale-invariant feature transform (SIFT) feature in the traditional BoF method,  extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically,  study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the complete local binary pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8*8, and the support vector machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images.
['Yixuan Yuan', 'Baopu Li', 'Max Q.-H. Meng']
I am writing a research paper on a idea Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images
[{"from": "human", "value": "I am writing a research paper on a idea Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Wireless capsule endoscopy (WCE) needs computerized method to reduce the review time for its large image data. ,  propose an improved bag of feature (BoF) method to assist classification of polyps in WCE images. Instead of utilizing a single scale-invariant feature transform (SIFT) feature in the traditional BoF method,  extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically,  study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the complete local binary pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8*8, and the support vector machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images."}]