EFFICIENT IMAGE RETRIEVAL IN BIG DATABASE USING MULTI FEATURE DESCRIPTOR

Abstract

Image processing is processing of images by using any form of signal processing using mathematical operations for which an image or a video is input. The image processing output may be an image or a set of parameters based on the image. An image retrieval system for a large database of digital images is a computer system for searching, retrieving and browsing image and obtaining most similar images. From the retrieved image multi features like Color Histogram, HOG Transform, Local Binary feature and Gist. Each of these features need to transfer by applying Heat Kernel function to generate range with each of this feature mentioned to a range form a multiview hash and put it in hash table. Calculate hash for the image and this hash can be map for different images also. The existing method for image retrieval is Hashing technology. Hashing is used to index and retrieve items in a database because it is faster to find the items using the shorter hashed key than to find it using the original value. For most hashing strategies, the performance of retrieval vigorously relies upon the decision of the high-dimensional component descriptor. The disadvantage is Curse of dimensionality and does not search faster. The proposed methods are Multi Feature Descriptor and Multiview alignment Hashing. Multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization(NMF), which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data and it can easily searches the similar images in less interval of time and it is effective.. Hash within Hash technology is the future enhancement, the hash size is large partitioning the hash and obtaining the efficient result.