Grayscale Image Retrieval using DCT on Row mean, Column mean and Combination
#1

Grayscale Image Retrieval using DCT on Row mean,
Column mean and Combination



.pdf   Grayscale Image Retrieval.pdf (Size: 252.35 KB / Downloads: 5)

Abstract

Today in the age of information explosion, how to search appropriate data from huge information
pool has become vital issue. Images have giant share in this information pool. Because of easy
availability of imaging devices, millions of images are being added to image pool every day. Image
retrieval deals with searching relevant images from large image database. The paper presents novel
image retrieval techniques based on discrete cosine transform applied on row mean, column mean
and combination for feature extraction.

Introduction

In the past few years, there has been
tremendous growth in the database technology to
store, retrieve and process large number of
images [1],[15],[16]. Initially, images were
represented just by textual description and
observations. But these methods hardly captured
the vivid details of an image. This led to research
on the lines of automatically extracting features
of images for the purpose of efficient retrieval
and sequencing of images which is referred as
Content Based Image Retrieval (CBIR)
[3],[4],[14]. Main intention of CBIR is efficient
retrieval of images form a huge image database
based on some automatically extracted features.
These features are extracted from properties such
as shape, color and texture of query image and
the various images in the repository [7].

Discrete Cosine Transform

The discrete cosine transform (DCT) [5],[15]
is closely related to the discrete Fourier
transform. It is a separable linear transformation;
that is, the two-dimensional transform is
equivalent to a one-dimensional DCT performed
along a single dimension followed by a onedimensional
DCT in the other dimension.

DCT Row Mean Image Retrieval

Here first the row mean of query image is
obtained. Then the DCT row mean feature
vector of query image is obtained by applying
DCT on row mean. For image retrieval using
DCT row mean, these query image features are
compared with DCT row mean features of
image database by finding Euclidian distances
using the formula given as equation 1.
These Euclidian distances are sorted in
ascending order and result images are grouped
together to get the precision and recall using the
formulae as given below in equation 7 and
equation 8.

Results and Discussion

Five images from each category are provided
as a query and are compared to images sorted
according to values of Euclidian Distance with
cumulative increment of 2 images up to 100
images, to obtain precision and recall values. To
determine which technique of generating feature
vectors is best, the average values of precision
and recall of all the techniques are plotted as
shown in Fig. 3 and Fig. 4.
Reply

Important Note..!

If you are not satisfied with above reply ,..Please

ASK HERE

So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page
Popular Searches: least mean square algorithm ppt, how to alter datatype of a column in sql, mean of ppto in marksheet, column project rcc, projection mean in dances, what is mean by scoop in id fan, grayscale morphology,

[-]
Quick Reply
Message
Type your reply to this message here.

Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
  Image Processing & Compression Techniques (Download Full Seminar Report) Computer Science Clay 42 22,827 07-10-2014, 07:57 PM
Last Post: seminar report asees
  Hardware for image processing - Basics Eye – Human vision sensor ppt computer topic 0 7,756 25-03-2014, 11:12 PM
Last Post: computer topic
  Automated Storage/Retrieval System seminar class 3 3,017 02-09-2013, 11:09 AM
Last Post: uchconveyor
  sketch image match to digital image arma 1 1,499 30-06-2013, 12:24 PM
Last Post: Guest
  Image Segmentation Using Information Bottleneck Method seminar class 4 4,000 19-01-2013, 12:45 PM
Last Post: seminar details
  Digital Image Watermarking project report helper 3 5,654 19-12-2012, 11:48 AM
Last Post: seminar details
  A survey of usage of Data Mining and Data Warehousing in Academic Institution and Lib seminar class 1 2,118 29-11-2012, 12:56 PM
Last Post: seminar details
  digital image processing project topics 1 2,276 19-11-2012, 01:46 PM
Last Post: seminar details
  IMAGE COMPRESSION USING WEDGELETS seminar class 4 3,516 08-11-2012, 12:44 PM
Last Post: seminar details
  Fuzzy Random Impulse Noise Removal From Color Image Sequences computer girl 1 1,686 24-10-2012, 01:45 PM
Last Post: seminar details

Forum Jump: