(Image Retrieval—Query by Example) Search for a “similar” image (similarity by visual content,…

(Image Retrieval—Query by Example) Search for a “similar” image (similarity by visual content, not by textual description) in a given data base of images. The used data base should contain at least, say, 300 images. Your program allows one to submit any image (not necessarily already in the given database), and the output is a subset of the data base (say, 10 images) sorted by similarity. Define “similarity” based on the distance between the descriptors of image features. The number of pairs of descriptors being “nearly identical”, scaled by the total number of detected keypoints, defines a measure of similarity. Figure illustrates such individual best-point-by-point matches between keypoints having “nearly identical” SURF-descriptors. The figure illustrates that those matches do not define one consistent affine (or perspective) transform, such as illustrated in Fig., where we assumed similarity between global patterns of keypoints. Include also the “typical colour” at a keypoint into the descriptor of the used feature detector (e.g. the mean colour in a small neighbourhood). Generate these extended descriptors for the images in your data base. The algorithmically interesting task now is the following: for a submitted image (“query by example”), calculate the extended descriptors for this image and aim at detecting the “most similar” images in the data base having the similarity definition in mind as given above. In other words, you have to design a particular classifier for solving this task. The use of KD-trees (not described in this book; see other sources for their definition) can speed up the involved comparison of higher-dimensional vectors. If the data base can be clustered into classes of “different” images, then a first step might be to calculate the Mahanalobis distance (not described in this book; see other sources for its definition) between the input image and the given clusters for identifying then the best matches in the cluster that minimizes the Mahanalobis distance.

(Image Retrieval—Query by Example) Search for a “similar” image (similarity by visual content, not...

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