Image Mining using SIFT Based Object Identification and Tagging

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Abstract - Voluminous image data that are accumulated in the organizations due to frequent occurrence of various events poses tremendous challenge in terms of storage and access. This image data could be transformed into useful information, if it is analyzed. Image mining deals with extracting embedded details, patterns and their relationship in images. Embedded details in the image could be extracted using high-level features that are robust to geometric and photometric changes. Objects are the building blocks of an image. Scale and rotation invariant features namely key points of an object that signify a scene could be extracted to identify and tag the images for classification and mining. The first step aims at identification of key objects that represent a scene and extract key points from these objects. This will serve as reference set. The next step is to process the test images, by extracting key points that are similar to the key object's key points using KNN classifier. The third step is to match the two set of key points that are similar by extracting lowlevel features in the neighborhood. If the number of matching points are above a threshold that is proportional to the number of interest points in the key objects, the image is tagged with the label that describe the scene. Images could then be classified and stored as an archive to enable query based retrieval and mining.

3 years ago


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