期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN:2158-107X
电子版ISSN:2156-5570
出版年度:2019
卷号:10
期号:12
页码:603-630
出版社:Science and Information Society (SAI)
摘要:It has been proven that the massive dataset is
strictly complex in Content Based image Retrieval (CBIR)
because the present strategies in CBIR might have faced
difficulties in feature extraction of the images. Moreover,
technological constraints encountered in the analysis and
extraction of the image arrays are how the system customizes the
primitive geometric structures known as polygonal
approximations structure. Hence, this study has discovered that
image feature extraction is utilized by applying the Principal
Component Analysis (PCA) technique, which is primarily based
on the matrix of image representation that will enlarge the
similarity of detection. The PCA approach needs to be enhanced
resulting from the lack of the extraction of features in songket
motives images. Therefore, this study proposes a new hybrid
model that will integrate PCA with geometric techniques for
image feature extraction to increase the recall and precision
result. This paper employs the use of a qualitative experimental
design model that involves three phases of activities. First, the
analysis and design phase, secondly is a development phase, and
lastly is the testing and evaluation phase. This paper focuses on
those two phases in terms of design and development phases. The
outcome process of the empirical phase is followed by designing
the algorithm and model based on the result of literature review.
This study has found that the hybrid between the principal
component analysis model and the geometry technique will help
to reduce the problems faced by the basic engineering technique
model, which is the constraint in analysing and extracting the
image features to customize the geometric primitive structure.