Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.

1. IMAGE SEARCH ENGINE
Presented to: Presesnted by:
Mr. Sanjeev Patel Avanish Kr. Singh (9910103451)
Mr. Himanshu Mittal

2.  Image search engine is a type of search engine specialised on
finding pictures, images, animations etc. Like the text search,
image search is an information retrieval system designed to help
to find information on the Internet and it allows the user to look
for images etc. using keywords or search phrases and to receive
a set of thumbnail images, sorted by relevancy.
 Types Of Search Engine:
Image Meta Search- search of images based on associated
metadata such as keywords, text, etc.
 Content-based image retrieval (CBIR) –CBIR aims at avoiding the
use of textual descriptions and instead retrieves images based
on similarities in their contents (textures, colors, shapes etc.) to
a user-supplied query image or user-specified image features.

3.  Color histogram: A colour histogram is a type of bar
graph, where each bar represents a particular colour of
the colour space being used.
 Texture: It contains important information about the
structural arrangement of the surface, such as; clouds,
leaves, bricks, fabric, etc.
 Edge Detection: Edge detection is the name for a set of
mathematical methods which aim at identifying points
in a digital image at which the image brightness
changes sharply or, more formally, has discontinuities.

4. example of edge detection:

5.  Digital Image Processing
 A Framework of Web Image Search Engine(RESEARCH
PAPER)
 An Effective Content-based Web Image Searching Engine
Algorithm(RESEARCH PAPER)
 http://tarekmamdouh.hubpages.com/hub/Global-and-
Local-Color-Histogram - hub
 http://tarekmamdouh.hubpages.com/hub/Image-
Retrieval-Color-Coherence-Vector
 Introduction to matlab

6.  (a) Text based image comparison algorithm
 (b) semantic-gap in the literature, is a gap
between inferred understanding / semantics
by pixel domain processing using low level
cues and human perceptions of visual cues of
given image.

7.  Histogram Approach:
 GCH (Global Color Histogram): Problem with GCH is that it
doesn’t include information about color spatial
distribution.
 LCH(Local Color Histogram): Main disadvantage with LCH
is it never give you two same images are equal if one of
them is rotated.
 histograms for classification is that the representation is
dependent of the color of the object being studied,
ignoring its shape and texture. Color histograms can
potentially be identical for two images with different
object content which happens to share color information.

8. The problem involves comparative study between different feature
detection techniques and entering an image as a query into a software
application that is designed to employ CBIR techniques in extracting
visual properties, and matching them. This is done to retrieve images
that are visually similar to the query image.

9.  There are two major steps involved in image
comparison,So based on that I have divided my
project into two parts:
 Feature Extraction
 Feature Matching
 Talking about Feature Extraction I have divided
my project into three sub parts(color,edge and
texture),each of which includes two different
algorithms ,one for feature extraction and
another for feature matching

10.  A) color
Histogram:

11.  Texture:

12.  Edge:

13. Colour:
 The color histogram can be built for any kind
of color space, although the term is more
often used for three-dimensional spaces like
RGB or HSV.
 A histogram is created consisting of number
of bins on x-axis and and pixel insenties on
y-axis.
 Here we have used RGB model.

14.  A)Sobel The operator consists of a pair of 3×3 convolution kernels as
shown in Figure 1. One kernel is simply the other rotated by 90°.
These kernels are designed to respond maximally to edges running
vertically and horizontally relative to the pixel grid, one kernel for each
of the two perpendicular orientations. The kernels can be applied
separately to the input image, to produce separate measurements of the
gradient component in each orientation (call these Gx and Gy). These
can then be combined together to find the absolute magnitude of the
gradient at each point and the orientation of that gradient. The gradient
magnitude is given by:

15. 

 which is much faster to compute.
 The angle of orientation of the edge (relative
to the pixel grid) giving rise to the spatial
gradient is given by:

16. a).Energy Level Algorithm:
1. Decompose the image into four sub-images
2. Calculate the energy of all decomposed images at the same scale,
using :
where M and N are the dimensions of the image, and X is the intensity
of the pixel located at row i and column j in the image map.
3. Repeat from step 1 for the low-low sub-band image, until index ind
is equal to 5. Increment ind.
Using the above algorithm, the energy levels of the sub-bands were
calculated, and further decomposition of the low-low sub-band image.
This is repeated five times, to reach fifth level decomposition. These
energy level values are stored to be used in the Euclidean distance
algorithm.

17.  Color: Quadratic distance

18.  Texture and edges: Euclidean Distance