Mining User Queries
with Markov Chains: Application to Online Image Retrieval
ABSTRACT:
We propose a novel method for automatic annotation,
indexing and annotation-based retrieval of images. The new method, that we call
Markovian Semantic Indexing (MSI), is presented in the context of an online
image retrieval system. Assuming such a system, the users’ queries are used to
construct an Aggregate Markov Chain (AMC) through which the relevance between
the keywords seen by the system is defined. The users’ queries are also used to
automatically annotate the images. A stochastic distance between images, based
on their annotation and the keyword relevance captured in the AMC is then
introduced. Geometric interpretations of the proposed distance are provided and
its relation to a clustering in the keyword space is investigated. By means of a
new measure of Markovian state similarity, the mean first cross passage time (CPT),
optimality properties of the proposed distance are proved. Images are modeled
as points in a vector space and their similarity is measured with MSI. The new
method is shown to possess certain theoretical advantages and also to achieve
better Precision versus Recall results when compared to Latent Semantic Indexing
(LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in
Annotation-Based Image Retrieval (ABIR) tasks.
EXISTING SYSTEM:
Annotation-Based Image Retrieval (ABIR) systems are
an attempt to incorporate more efficient semantic content into both text-based queries
and image captions (i.e. Google Image Search, Yahoo! Image Search). The Latent
Semantic Indexing (LSI)-based approaches that were initially applied with
increased success in document indexing and retrieval were incorporated into the
ABIR systems to discover a more reliable concept association.
DISADVANTAGES
OF EXISTING SYSTEM:
While the former gap brings in the issue of users’
interpretations of images and how it is inherently difficult to capture them in
visual content, the latter gap makes recognition from image content challenging
due to limitations in recording and description capabilities.
A reason for this lies in the sparsity of the
per-image keyword annotation data in comparison to the number of keywords that
are usually assigned to documents.
PROPOSED SYSTEM:
We introduce the Markovian Semantic Indexing (MSI),
a new method for automatic annotation and annotation based image retrieval. The
properties of MSI make it particularly suitable for ABIR tasks when the per
image annotation data is limited. The characteristics of the method make it
also particularly applicable in the context of online image retrieval systems.
ADVANTAGES
OF PROPOSED SYSTEM:
The targeting is more accurate, compared to other
systems that use external means of non-dynamic or non-adaptive nature to define
keyword relevance.
MSI achieves better retrieval results in sparsely
annotated image data sets. A comparison to LSI on 64 images gathered from the Google
Image Search and annotated in a transparent way by the proposed system,
revealed certain advantages for the MSI method, mainly in retrieving images
with deeper dependencies than simple keyword co-occurrence
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium IV 2.4 GHz.
·
Hard Disk : 40 GB.
·
Monitor :
15 inch VGA Colour.
·
Mouse :
Logitech Mouse.
·
Ram : 512 MB
·
Keyboard :
Standard Keyboard
SOFTWARE REQUIREMENTS:
·
Operating System : Windows XP.
·
Coding Language : ASP.NET, C#.Net.
·
Database :
SQL Server 2005
REFERENCE:
Konstantinos A. Raftopoulos,Member, IEEE, Klimis S.
Ntalianis, Dionyssios D. Sourlas, and Stefanos D. Kollias,Member, IEEE “Mining
User Queries with Markov Chains: Application to Online Image Retrieval” -
IEEE TRANSACTIONS ON
KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 2, FEBRUARY 2013.
