Explain several approaches have been proposed and studied for similarity-based retrieval in image databases, based on image signature.

 Several approaches have been proposed and studied for similarity-based retrieval in image databases, based on image signature:

Color histogram-based signature: In this approach, the signature of an image histograms are based on the color composition of an image regardless of its scale or orientation. This method does not contain any information about shape, image topology, or texture. Thus, two images include color with similar color composition but that contain very different shapes or textures may be identified as similar, although they could be completely unrelated semantically.

 Multi-feature composed signature: In this approach, the signature of an image includes a composition of multiple features: color histogram, shape, image topology, and texture. The extracted image features are stored as metadata, and images are indexed based on such metadata. Often,. separate distance functions can be defined for each feature and subsequently combined to derive the overall results.

Wavelet-based signature: This approach uses the dominant wavelet coefficients of an image as its signature. Wavelets capture shape, texture, and image topology information in a single unified framework. This improves efficiency and reduces the need for providing multiple search primitives (unlike the second method above). 

Wavelet-based signature with region-based granularity: In this approach, the computation and comparison of signatures are at the granularity of regions, not the entire image. This is based on the observation that similar images may contain similar regions, but a region in one image could be a translation or scaling of a matching region in the other.