Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : image retrieval

Word Retrieval based on FREAK Descriptor to Identify the Image of the English Letter that Corresponds to the First Letter of the Word

Ekhlas F. Naser

Engineering and Technology Journal, 2020, Volume 38, Issue 3B, Pages 150-160
DOI: 10.30684/etj.v38i3B.1511

For the reason of colossal technological developments, the requirement of image information methods became a significant issue. The aim of this research was to retrieve the word based on Fast Retina Key-points (FREAK) descriptor .The suggested system consists of four stages. In the first stage, the images of English letters are loaded. Points are detected via SUSAN in the second stage. FREAK used in the third stage and then a database was created containing 26 English letters. The image to be tested was entered and the points are extracted in the fourth stage and then Manhattan distance was used to calculate the distance between the value of the test image descriptors and all the values of the descriptors in a database. The experimental results show that the precision and the recall values were high for retrieval of the words when using SUSAN because it extracts a large number of interest points compared to the Harris method. For example, for the letter H was 104 with SUSAN while it was 42 for Harris, therefore; the precision for retrieval of the word Hour was 89% and recall was 93% when using SUSAN while precision was 77% and recall was 80% when using Harris.

Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

Beshaier A. Abdulla; Yossra H. Ali; Nuha J. Ibrahim

Engineering and Technology Journal, 2020, Volume 38, Issue 5, Pages 719-727
DOI: 10.30684/etj.v38i5A.519

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used. And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

Ontology Based Image Analysis in Autonomous Computer Environment

Mohammed Gheni Alwan; Ali Shawket Thiab; Ragheed Dawood Salim

Engineering and Technology Journal, 2013, Volume 31, Issue 4, Pages 419-430

Human visual system can interpret and perceive images at different levels where colour, shape, texture and object detection are low level features interpreted well by healthy human visual system, in the same manner detected objects are perceived according to ontological approach.
This paper is devoted to present Content Based Image Retrieval system as continues efforts to bridging the semantic gap between semantic concepts and low level feature of images.
The proposal presented by this paper is focusing on investigating images for conceptual objects topology, by integrating knowledge of multiple Agents collaborated to abstract images into concepts.
Agents individually or collaboratively will promote low level image features to semantic concepts and these concepts will be subjected to certain ontology designed in specific domain to semantically index this image and later to be retrieved according to special query accommodating indexing strategy.

Image Features Evaluation Using New Algorithm Proposed For Reducing Image Feature Number & Size Stored In Database

Shahlaa T. Abdulwahab

Engineering and Technology Journal, 2011, Volume 29, Issue 6, Pages 1176-1194

This study proposes technique that capable of reducing image features size and
number stored in the database. The proposed technique depends on the image content
of numerical values for the three basic colors (red, green and blue) and then stores it in
the database and to be used for image retrieval. This technique has been developed
based on recent image retrieval procedures that include Color Descriptor Matrix,
YCbCr Color Space and Discrete Cosine Transform. Those procedures have been
applied sequentially on the image and finally Kekre’s Transform has been applied in
the last stage of this technique to evaluate image features and reduce its stored size in
the database. The validity and accuracy of the proposed technique have been
evaluated through experiments by applying Kekre’s Transform on Color Descriptor
Matrix instead of using Kekre’s Transform directly on the image in order to reduce its
feature stored size. Another experiments have been tested and evaluated that include
the application of YCbCr Color Space on the Color Descriptor Matrix and
finally Kekre’s Transform to be executed and explore the image features size and
compare it with the previous stage. The effect of applying the Discrete Cosine
Transform on the YCbCr Color Space and finally the Kekre’s Transform on the image
features size has been studied and compared with the previous step. It is concluded that
the best reduction in image features size stored in the database can be obtained only
when Kekre’s Transform applied in the last step of the proposed technique with
unchanged threshold based image retrieval ratios. Parametric study has been conducted
to investigate the effect of applying the new algorithm on both isolated and mixed
image groups. Good precision ratios of 82% and 65% have been obtained for the
isolated and mixed image groups respectively.