Description

ABSTRACT:

                With hash codes, scalable image search can be performed in hamming space using

hamming distance. We propose a novel approach that computes query-adaptive weights for each

bit of the hash codes. First, for images we calculate finer-grained hash code level with the bitwise

weights. Each hash code is expected to have a unique similarity to the queries. The results can be

rapidly ranked by weighted hamming distance.  Scale-invariant feature transform (or sift) is an

algorithm to detect and describe local features in images. Lowe’s method for image feature

generation transforms an image into a large collection of feature vectors, each of which is

invariant to image translation, scaling, and rotation, partially invariant. The minimum distance

index images are retrieved from the database. Initially we remove the noise from the image by the

use Gaussian filter. Gaussian filter is windowed filter of linear class; by its nature is weighted

mean.