### Image content analysis

```Image content analysis
Location-aware mobile applications
development
Spring 2011
Paras Pant
Overview
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Introduction
Basic Image Analysis
Content-Based Image Retrieval
Some location based system.
Introduction
• Nowadays, the analysis of information has become
paramount importance.
• Every image carries a huge amount of information
but only a small part of it is relevant for a certain
application.
• An image can be, grayscale or color, clear or foggy
etc.
• Our interest is to analyze the content of the digital
image.
Basics
• how many object.
• how many color object
• how many green.
Threshold
• Separate image object for the background.
– O1 = {f(m,n):f(m,n)>T} (object pixels)
– B2 = {f(m,n):f(m,n)<=T} (background pixels)
– (m,n) value of pixel at some location, T threshold
value
• Different thresholding algorithm exist.
• Global or local thresholding.
Gray Scale Image
Thresholding
Morphological operation imopen
Morphology operations on images
• The techniques used on these binary images.
• The foundation of morphological processing is in the
mathematically rigorous field of set theory
http://www.dspguide.com/ch25/4.htm
Image
Threshold in every channel and
combined
Morphological operation imopen
Color
Mean color given to the object
Green color are given as feature
and we extract the object
Clustering
• Grouping based on
relevant information.
K-mean Clustering
1. Define a cluster Centroid.
2. Assign each object to the group that
has the closest centroid.
3. When all objects have been assigned,
recalculate the positions of the K
centroids.
4. Repeat Steps 2 and 3 until the
centroids no longer move. This
produces a separation of the objects
into groups from which the metric to
be minimized can be calculated.
ContentBased Image Retrieval (CBIR),
• Content-independent metadata: data that is
not directly concerned with image content,
but related to it. Examples are image format,
author’s name, date, and location
• Content based metadata: such as color,
texture and shape.
Content Based Image Retrieval
Images
pre
processing
Query Image
Preprocessing
Feature
Calculation
Database
Feature
Calculation
Similarity
measure
output
Color based Feature and Similarity
measure
• Histogram
– Chromaticity histogram
• Preprocessing
– Color space
– Color Constancy
» feature of the human color perception system which ensures
that the perceived color of objects remains relatively
constant under varying illumination conditions.
• Similarity measure
– Distance Calculation
– Cosine similarity
– Histogram intersection
Transform image into different space
• CIE L* a* b*
– Where L is Lightness, a &
b color chromaticity
• HSV
• rg chromaticity
r=R/R+G+B
g=G/R+G+B
Image Retrieval
Images
Color
Constancy
Query Image
Color
Constancy
Database
Histogram
Calculation
Histogram
Calculation
Histogram
Intersection
output
Retrival results
Query image
Result 2
Query image
Location Aware Application
• Image based location Awareness
– http://137.189.32.220/welcome_files/Page391.ht
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