This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.

1. INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395 -0056
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IMAGE BASED SEARCH ENGINE
Harshita Bavise1,Kavita Kaithwas2,Neha Nagpure3
Assistant Prof. Mitali Ingle4
123UG Student,B.E.,Computer Science and Engineering,DBACER,Nagpur,maharashtra,India
4 Assistant professor,B.E.,Computer Science and Engineering,DBACER,Nagpur,maharashtra,India
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Abstract— Web In the Existing search engines the
accuracy of retrieving the document using the image is low.
It is inefficient in the retrieval of documents. The aim of the
image search is to retrieve the relevant image with respect
to user query from a large image database. With the
popularity of the network and development of multimedia
technology, the traditional information retrieval
techniques do not meet the users demand. Recently, the
content-based image retrieval has become the hot topic
and the techniques of content-based image retrieval have
been achieved great development. In this document, the
basic components of content-based image retrieval system
are introduced. In many areas of commerce, government, A
web search engine is a software system that is designed to
search for information on the World Wide academia, and
hospitals, large collections of digital images are being
created. Image retrieval methods based on color, texture,
shape and semantic image are discussed, analyzed and
compared.
Criminal record generally contains personal information
about particular person along with photograph. To identify
any criminal we need some identification regarding
person, which are given by eyewitnesses. In most cases the
quality and resolution of the recorded image-segments is
poor and hard to identify a face. To overcome this sort of
problem we are developing software.
1.INTRODUCTION:
The basic components, to be discussed in this chapter, and
the corresponding dataflow process is sections in this
chapter harmonize with the data as they flow from one
computational component to another as follows:
Interactive query formulation:
Interactive query formulation is offered either by query
(sub)image(s) or by offering a pattern of feature values
and weights. To achieve interactive query formulation, an
image is recorded or selected from an image repository.
With the query formulation, the aim to search for
particular images in the database. The mode of search
might be one of the following three Overview of the basic
concepts of the content-based image retrieval scheme as
considered in this chapter. First, collect database and
create login, registration form. Then face detect from input
image using open cv which is given by user These image
feature are used to find the images in the database which
are most similar. Then, a candidate list of most similar
images is shown to the user. From the user feed-back the
query is optimized and used as a new query in an iterative
manner.
A web search engine is a software system that is designed
to search for information on the World Wide Web. In the
Existing search engines the accuracy of retrieving the
document using the image is low. It is inefficient in the
retrieval of documents. The aim of the image search is to
retrieve the relevant image with respect to user query
from a large image database. With the popularity of the
network and development of multimedia technology, the
traditional information retrieval techniques do not meet
the users demand. Recently, the content-based image
retrieval has become the hot topic and the techniques of
content-based image retrieval have been achieved great
development. In this document, the basic components of
content-based image retrieval system are introduced. In
many areas of commerce, government, academia, and
hospitals, large collections of digital images are being
created. Image retrieval methods based on color, texture,
shape and semantic image are discussed, analyzed and
compared. Feature detection is the process where we
automatically examine an image to extract features that
are unique to the objects in the image, in such a manner
that we are able to detect an object based on its features in
different images. This detection should ideally be possible
when the image shows the object with different
transformations, mainly scale and rotation, or when parts
of the object are occluded. To improve the performance of
search, labeling information is collected from user and new
method is proposed to actively select more informative
query images through structural information. Few images
are labeled by user in active re-ranking.
2.CATEGORIES:
Search by association, target search, and category search.
For search by association, the intention of the user is to
browse through a large collection of images without a
specific aim. Search by association tries to find interesting
images and is often applied in an iterative way by means of
relevance feedback. Target search is to find similar (target)
images in the image database and information. Note that
“similar image” may imply a (partially) identical image, or
a (partially) identical object in the image.

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INPUT IMAGE:
Take image and text as input from user. Input images we
take for image search is to retrieve the relevant image and
information with respect to user query from a large.
SIMILARITY COMPARISON:
Compare similarities with web search engine image.
comparing images to find similar images in a database .to
identify the search images is available or not in database. if
search image is available in database then retrieve the all
information related to that image.
RETRIEVE INFORMATION & IMAGES :
Show related information and image of input image. After
comparing the image with the database if that image match
the we retrieve information and perform operation on that
retrieved data.
3.PROPOSED WORK:
3.1MODULES
Data Creation (Details, Image):-
Take input image and other details as input user form.
Input images we take for image search is to retrieve the
relevant image with respect to user query from a large.
Face Detection:-
After taking input data we are detecting face using open
CV. If face is found then go for next step i.e. compare the
detail and image name. if search is not found then go for
another image search.
Compare the details & Image name:-
compare the detail and image name from the dataset.
4.FLOW DIAGRAM:
5.References:
[1] Nikolaos G. Bourbakis, “Image Data Compression
Encryption Using G-Scan Patterns”, IEEE 0-7803-
4053-1/97, pp. 117-1120, 1997.
[2] S. S. Maniccam, and N. G. Bourbakis, “SCAN
BasedLossless Image Compression and
Encryption”, IEEE 0-7695-0446-9/99, pp. 490-499,
1999.
[3] Howard Cheng and Xiaobo Li, “Partial Encryption of
Compressed Images and Videos”, IEEE
Transactions On Signal Processing, Vol. 48, No. 8,
pp. 2439-2451, August 2000.
[4] Ebru Celikel and Mehmet Emin Dalkilic,
“Experiments on A Secure Compression Algorithm”,
start
Input Data(Details,Images)
Face detection
IF face
is found
Search
another
image
Informati
-on not
available
Informa
-tion
remove
If
(search
= =true)
Compare the details &
image name
stop

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© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1982
Proceedings of the International Conference on
Information Technology: Coding and Computing
(ITCC’04), 2004.
[5] Masanori Ito, Noboru Ohnishi, Ayman Alfalou, and
Ali Mansour, “New Image Encryption And
Compression Method Based On Independent
Component Analysis”, IEEE, 2007.