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On Computing Local and Global Similarity in Images

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The retrieval of images based on their visual similarity to an example image is an important and fascinating area of research. Here, we discuss various ways in which visual appearance may be characterized for determining both global and local similarity in images. One popular method involves the computation of global measures like moment invariants to characterize global similarity. Although this means that the image may be characterized using a few numbers, the performance is often poor. Techniques based on moment invariants often perform poorly. They require that the object be a binary shape without holes which is often not practical. In addition, moment invariants are sensitive to noise. Visual appearance is better represented using local features computed at multiple scales. Such local features may include the outputs of images filtered with Gaussian derivatives, differential invariants or geometric quantities like curvature and phase. Two images may be said to be similar if they have similar distributions of such features. Global similarity may, therefore, be deduced by comparing histograms of such features. This can be done rapidly. Histograms cannot be used to compute local similarity. Instead, the constraint that the spatial relationship between the features in the query be similar to the spatial relationship between the features of its matching counterparts in the database provides a means for computing local similarity. The methods presented here do not require prior segmentation of the database. In the case of local representation objects can be embedded in arbitrary backgrounds and both methods handle a range of size variations and viewpoint variations up to 20 or 25 degrees.

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  • Information Science
  • Computer Systems

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