Image retrieval using Bag of features

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Image retrieval using Bag of features

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Image retrieval using Bag of features

 Image retrieval (CBIR) is still an active research field. There are a number of approaches available to retrieve visual data from large databases. But almost all the approaches require an image digestion in their initial steps. Image digestion is describing an image using low level features such as color, shape, and texture while removing unimportant details. Color histograms, color moments, dominant color, scalable color, shape contour, shape region, homogeneous texture, texture browsing, and edge histogram are some of the popular descriptors that are used in CBIR applications. Bag-Of-Feature (BoF) is another kind of visual feature descriptor which can be used in CBIR applications. In order to obtain a BoF descriptor, we need to extract a feature from the image. This feature can be any thing such as SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Features), and LBP (Local Binary Patterns), etc.

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