- Content-Based Image Retrieval (CBIR) is a technique used to retrieve images from large databases based on their visual content. It involves extracting features from an input query image and finding similar images from the database based on extracted features. - The paper proposes a CBIR technique based on color feature extraction, where the queried image is divided into parts and color features are extracted to form a feature vector, which is then compared to feature vectors of images in the database to find similar images. - The technique currently only uses color as the feature for similarity comparison, which limits its effectiveness, so future work involves combining multiple features like texture and shape for more accurate image retrieval.

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Content-Based Image Retrieval (CBIR) in Digital Libraries
Using Color Feature
Ali Abdulla1
, Gayathri S 2
, Vijayanthi V 3
1
15MSC0020, 2
15MSC0011, 3
15MSC0007
Faculty Supervisor : Prof. Anisha M. Lal (SCOPE)
ABSTRACT
Set Conference
SET ID:
12SETMSC0541
INTRODUCTION
As the expansion of multimedia technology increases, people observing an evolution
from everything, starting from small database to image database. Nowadays, this trend
reach up to digital libraries, whereby resources are accessible in digital format rather
than in printed format or hard copy. The contents of these libraries can range from
digital image, text, video, sound, etc. In this case, users are not willing and also will not
understand on to continue using traditional ways to access and retrieve such kind of
information. In that scenario, they need special ways or technique in order to help them
to accomplish this task. Content Based Image Retrieval (CBIR) is the main technique
that is mostly used to retrieves the images from such large databases. It reflects on the
image provided by a user as a query in which it pictorial figure manipulated and then
applied for content search and the results given back to the user. Many researches
already conducted to find a proper way on how images can be retrieved in efficient way
from large database in different domain applications. In this paper, we propose a CBIR
technique based on the color feature in which the queried image studied and
compared with available images inside the image library database
▪ CBIR is a process that intend to explore particular images from a large database
related to a specified query
▪ This technique reflects on the image provided by a user as a query in which it
pictorial figure manipulated and then applied for content search
▪ The CBIR technique is much faster than the way persons differentiate between
images, this is because it distinguishes different parts available in an image based
on their matches of shape, color, texture, pattern, etc. and then chooses the
resemblances of these two particular images by estimate the nearness of these
different parts
▪ Most of the CBIR system operates on the same way whereby a feature vector
from each image are mined from the storage database.
▪ After that, all agreed feature vectors arranged and used as an index for database
queries
▪ When user sends a query, a feature vector is mined from the requested query and
it will be compared to the feature index that is available in the database.
▪ If the features match then the query image will return the desired image that was
looked by a particular user. The essential difference between different CBIR
systems is which features a particular system is going to use to extract the image
and by using which algorithm these feature vectors were compared.
COLOR FEATURE
EXTRACTION OF COLOR FEATURES
PROPOSED TECHNIQUE
CONCLUSION AND FUTURE WORK
▪ In this paper, we present the proposed CBIR technique based on the
color feature in which the queried image is studied and compared with
available images in the database.
▪ In order to make calculation simple, the images to be compared (image
query and database images) divided into n equal parts and the feature
vectors are calculated and the distance between them is calculated
▪ Few steps were presented oh how this similarity of the images are
calculated
▪ But also the technique has some limitation of searching images by using
only one feature (color feature), the situation seemed to be limited for
some images since there are some other features to be compared such
as shape and texture.
▪ Our expectations in future is to implement this proposed technique
▪ Matlab software will be chosen as a tool to read and get the values from
the images.
▪ Also the study will be extended by introducing the combination of many
features for comparison such as texture and shape in order to get
desirable output from the queried image used to search particular image.
Figure 1 – Content Based Image Retrieval (CBIR) Framework
▪ Color is the most essential factor for CBIR
▪ Makes the images to be easy recognized by human and computer
whenever it is required to compare one image from another.
▪ Many characteristics to consider while extracting color feature
▪ Most important one is the selection of a Color space.
▪ RGB is a good example of Color Space
▪ It allots to every picture element (pixel) a three-element vector
providing the color strengths of the basic three key colors that are
RED, GREEN and BLUE.
▪ By using Transformation method on RGB, we can produce another
color space.
▪ One among the spaces created from RGB is HSV (Hue, Saturation and
Value or Brightness)
▪ The aims is to get the color space that is the same as human color
perception
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REFFERENCES
Figure 2 - Proposed CBIR based on color similarity
Measurement of color similarities