Image reconstruction using Content Based Image Retrieval
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Garima Joshi, Bhawana Mauraya
|DOI : 10.14445/22315381/IJETT-V49P250|
Garima Joshi, Bhawana Mauraya "Image reconstruction using Content Based Image Retrieval", International Journal of Engineering Trends and Technology (IJETT), V49(5),330-334 July 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
The paper presented here contains a method of reconstructing a damaged image. This image is damaged as it has patches and to get the complete and clear image this patch is removed from that image. But to remove that patch the same image which is not damaged should be present in a database. This database may contains a large number of images in it to find the most similar image and reconstruct this patch several techniques are summed with the CBIR method. The results achieved in this whole process are 97.71% accurate. This accuracy is checked by the intensity values of the signals form images.
 A.Anandh, Dr.K.Mala and S.Suganya, “Content Based Image Retrieval System based on Semantic Information Using Color, Texture and Shape Features”, IEEE 2016.
 Amit Singla and MeenakshiGarg, “CBIR Approach Based On Combined HSV, Auto Correlogram, Color Moments and Gabor Wavelet”, International Journal of Engineering And Computer Science 2014.
 Mrs. M. D. Malkauthekar, “ANALYSIS OF EUCLIDEAN DISTANCE AND MANHATTAN DISTANCE MEASURE IN FACE RECOGNITION”.
 Ruigang Fu, Biao Li, YinghuiGao, Ping Wang, “Content-Based Image Retrieval Based on CNN and SVM”, IEEE 2016.
 Dhruvi M Shah and Prof. Urmi Desai, “A Survey on Combine Approach of Low Level Features Extraction in CBIR”, IEEE 2017.
 NidhiTripathi, PankajSharna and Manish Gupta, “A New Technique For CBIR with Contrast Enhancement using Multi-Feature and Multi Class SVM Classification”, IEEE 2016.
 C. Benavides, J. Villegas, G. Román and C. Avilés, “Face Classification by Local Texture Analysis through CBIR and SURF Points”, IEEE 2016.
 ArdalanBenam, Mark S. Drew and M. Stella Atkins, “A CBIR SYSTEM FOR LOCATING AND RETRIEVING PIGMENT NETWORK IN DERMOSCOPY IMAGES USING DERMOSCOPY INTEREST POINT DETECTION”, IEEE 2017.
 Xiaolin Chen, Xiaokang Yang, Rui Zhang, Anwen Liu, and ShibaoZheng, “Edge Region Color Autocorrelogram: A New Low-level Feature Applied in CBIR”.
 NingthoujamSunita Devi and K.Hemachandran, “Retrieval and Recognition of faces using Content-BasedImage Retrieval (CBIR) and Feature Combination method”, IEEE 2016.
 Saurav Seth, PrashantUpadhyay, Ruchit Shroff and RupaliKomatwar, “Review of Content Based Image Retrieval Systems”, International Journal of Engineering Trends and Technology (IJETT) – Volume 19 Number 4 – Jan 2015.
CBIR, Pixilation, Filteration.