Essay on Image Search Using Hash Code

Essay on Image Search Using Hash Code

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This paper introduces an effective method for query
adaptive image retrieval using low-level feature extraction and
hashing method. The low-level feature primarily constitute
color, shape and texture features. For color feature extraction
color moments, color Histogram and color correlogram
method were implemented and for texture feature extraction
used wavelet moment method. Hashing methods is used to
embed high-dimensional image features into Hamming space,
where search can be performed in real-time based on
Hamming distance of compact hash codes. Depending upon
minimum Hamming distance it returns the similar image to
query image.
Index Terms—Hash Code, Hamming distance, Hamming
WEB data including documents, images and videos are
increasing rapidly. Consider an example of website Flickr
mainly used for photo sharing and it consist over 5 billion
images. For finding relevant image from such massive
databases we need some easy method. Moreover search
engine like Google and Bing based on textual input;
content-based image retrieval (CBIR) has attracted
substantial attention over the past decade. Instead of taking
textual keywords as input, CBIR techniques directly take a
visual query image Q and try to return images which are
closer to query image from a given database X using a prespecified
feature space and distance measure.
Generally a large-scale image search system consists of
two key components “an effective image feature
representation and an efficient search mechanism”.
Basically the quality of image searching results relies
heavily on the representation power of image features [1].
The efficient search mechanism is critical when existing
image features are mostly of high dimensions and current
image ...

... middle of paper ... Hash
Codes” IEEE Transactions On Multimedia, Vol. 15, No. 2,
February 2013
[2] Jun Wang, Sanjiv Kumar and Shih-Fu Chang” Semi-
Supervised Hashing for Large Scale Search” IEEE Transactions
On Pattern Analysis And Machine Intelligence, Vol. 34, No. 12,
December 2012
[3] Chenxia Wu, Jianke Zhu, Deng Cai, Member, Chun Chen, and
Jiajun Bu, “Semi-Supervised Nonlinear Hashing Using Bootstrap
Sequential Projection Learning” IEEE Transactions On
Knowledge And Data Engineering, Vol. 25, No. 6, June 2013
[4] Archana B. Waghmare” Low-Level Feature Extraction
for Content-Based Image Retrieval” International Journal of
Advances in Computing and Information Researches ISSN: 2277-
4068, Volume 1– No.2, April 2012
[5] Dr. H. B. Kekre, Mr.Dhirendra Mishra” Image Retrieval
using Image Hashing” Techno-Path: Journal of Science,
Engineering & Technology Management, Vol. 1 No.3 Oct

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