20-07-2013, 03:14 PM
Sketch4Match – Content-based Image Retrieval System Using Sketches
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Abstract
The content based image retrieval (CBIR) is one of
the most popular, rising research areas of the digital image pro-
cessing. Most of the available image search tools, such as Google
Images and Yahoo! Image search, are based on textual annotation
of images. In these tools, images are manually annotated with
keywords and then retrieved using text-based search methods.
The performances of these systems are not satisfactory. The goal
of CBIR is to extract visual content of an image automatically,
like color, texture, or shape.
This paper aims to introduce the problems and challenges
concerned with the design and the creation of CBIR systems,
which is based on a free hand sketch (Sketch based image
retrieval – SBIR). With the help of the existing methods, describe
a possible solution how to design and implement a task spesisc
descriptor, which can handle the informational gap between a
sketch and a colored image, making an opportunity for the
efscient search hereby. The used descriptor is constructed after
such special sequence of preprocessing steps that the transformed
full color image and the sketch can be compared. We have
studied EHD, HOG and SIFT. Experimental results on two
sample databases showed good results. Overall, the results show
that the sketch based system allows users an intuitive access to
search-tools.
INTRODUCTION
Before the spreading of information technology a huge
number of data had to be managed, processed and stored.
It was also textual and visual information. Parallelly of the
appearance and quick evolution of computers an increasing
measure of data had to be managed. The growing of data
storages and revolution of internet had changed the world. The
efsciency of searching in information set is a very important
point of view. In case of texts we can search ƀexibly using
keywords, but if we use images, we cannot apply dynamic
methods. Two questions can come up. The srst is who yields
the keywords. And the second is an image can be well
represented by keywords.
The Global Structure of Our System
The system building blocks include a preprocessing subsys-
tem, which eliminates the problems caused by the diversity
of images. Using the feature vector generating subsystem our
image can be represented by numbers considering a given
property. The database management subsystem provides an
interface between the database and the program. Based on the
feature vectors and the sample image the retrieval subsystem
provides the response list for the user using the displaying.
Conclusion
Among the objectives of this paper performed to design,
implement and test a sketch-based image retrieval system. Two
main aspects were taken into account. The retrieval process has
to be unconventional and highly interactive. The robustness of
the method is essential in some degree of noise, which might
also be in case of simple images.
The drawn image without modiscation can not be compared
with color image, or its edge representation. Alternatively a
distance transform step was introduced. The simple smoothing
and edge detection based method was improved, which had a
similar importance as the previous step.