%0 Journal Article %1 noauthororeditor %A Usha, Mrs.S.Gandhimathi @ %D 2014 %J International Journal of Computational Science and Information Technology (IJCSITY) %K (GLCM), (SIFT). CO-OCCURRENCE FEATURE GRAY INVARIANT LEVEL MATRIX SCALE TRANSFORM %N 2 %P 1 - 9 %R 10.5121/ijcsity.2014.2207 %T FEATURE EXTRACTION BASED RETRIEVAL OF GEOGRAPHIC IMAGES %U http://airccse.org/journal/ijcsity/papers/2214ijcsity07.pdf %V 2 %X This project is to retrieve the similar geographic images from the dataset based on the features extracted. Retrieval is the process of collecting the relevant images from the dataset which contains more number of images. Initially the preprocessing step is performed in order to remove noise occurred in input image with the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the features from the images. After this process, the relevant geographic images are retrieved from the dataset by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to perform reliable recognition, it is important that the feature extracted from the training image be detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in the applications such as detection and classification.

@article{noauthororeditor, abstract = {This project is to retrieve the similar geographic images from the dataset based on the features extracted. Retrieval is the process of collecting the relevant images from the dataset which contains more number of images. Initially the preprocessing step is performed in order to remove noise occurred in input image with the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the features from the images. After this process, the relevant geographic images are retrieved from the dataset by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to perform reliable recognition, it is important that the feature extracted from the training image be detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in the applications such as detection and classification. }, added-at = {2019-11-08T07:33:24.000+0100}, author = {Usha, Mrs.S.Gandhimathi @}, biburl = {https://www.bibsonomy.org/bibtex/22e93db65a1e58cdcef613d1c4e37dd56/anderson_sam}, doi = {10.5121/ijcsity.2014.2207}, interhash = {a7f84786105db5e6c07c51a4956cde46}, intrahash = {2e93db65a1e58cdcef613d1c4e37dd56}, issn = {ISSN : 2320-7442 (Online) ; 2320 - 8457 (Print)}, journal = {International Journal of Computational Science and Information Technology (IJCSITY)}, keywords = {(GLCM), (SIFT). CO-OCCURRENCE FEATURE GRAY INVARIANT LEVEL MATRIX SCALE TRANSFORM}, month = may, number = 2, pages = {1 - 9}, timestamp = {2019-11-08T07:33:24.000+0100}, title = {FEATURE EXTRACTION BASED RETRIEVAL OF GEOGRAPHIC IMAGES}, url = {http://airccse.org/journal/ijcsity/papers/2214ijcsity07.pdf}, volume = 2, year = 2014 }