Email this article 
Open Access
Subscription or Fee Access
Data Filter Crawler Based On Image Segmentation Technique Using EXIF Meta Image Tags
Abstract
Most image search engines are keyword-based, using keywords found in the filename or nearby the image or otherwise associated with it. Content-based image retrieval is the science if finding images by the actual content of an image, such as the colors or what objects are shown in the image. Basically it known as Image Search. An image search is a search engine that is designed to find an image. The search can be based on keywords, a picture, or a web link to a picture. The results depend on the search criterion, such as metadata, distribution of color, shape, etc., and the search technique which the browser uses.
Keywords
References
“An Introduction to Information Retrieval”, online edition (c) 2009 Cambridge UP, Draft April 1, 2009
Rutter, Chris. "What is metadata: copyright photos in 4 steps"? Digital Camera Magazine. Future Publishing.
”Flickr Images”, http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images
Mankad, Rujul, and Priyanka Buch. "Review on Content Based Video Retrieval." Digital Image Processing 7.1 (2015): 11-15
G. Pass and R. Zabih. Histogram refinement for contentbased image retrieval. IEEE Workshop on Applications of Computer Vision, pages 96–102, 1996.
J. Huang and S. R. Kumar, M. Mitra, W. Zhu, and R. Zabih. Image indexing using color correlograms. IEEE Workshop on Applications of Computer Vision, pages 1–7, 1997.
Saurav, Swapnil, Prajakta Belsare, and Siddhartha Sarkar. "Holistic Correlation of Color Models, Color Features and Distance Metrics on Content-Based Image Retrieval." (2015).
Sidhu, Supreet, and Jyoti Saxena. "CONTENT BASED IMAGE RETRIEVAL A REVIEW." (2015).
Zhang, Chi, and Lei Huang. "Study on Content-Based of Image Retrieval."LISS 2013. Springer Berlin Heidelberg, 2015. 591-594.
Boparai, Navreen Kaur, and Amit Chhabra. "A hybrid approach for improving Content Based Image Retrieval systems." Next Generation Computing Technologies (NGCT), 2015 1st International Conference on. IEEE, 2015.
Source Camera Identification for Mobile Phones using EXIF Data and Lens Features, Mr. Mukul Khurana Mr. Vivek Sharma Mr. Pulkit Mehndiratta Dr. Shelly Sachdeva 2015 International Journal of Computer Science Issues
The New Data and New Challenges in Multimedia Research,Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde,Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li, Yahoo Research LAB 2015
Social-oriented visual image search, Shaowei Liu, Peng Cui, Huanbo Luan b , Wenwu Zhu , Shiqiang Yang, Qi Tian c , Computer Vision and Image Understanding,Elsevier 2014
Proposal of Time-crawler which collects an event time by reading Exif data in blogs, Ismail Arai, Kazutoshi Fujikawa and Hideki Sunahara, IEEE 2008 IEEE Conference On Multisensor & Intelligent System,Japan
Metadata for Efficient Storage and Retrieval of Life Log Media , Han Hoon Kang, Chull Hwan Song, Young Chul Kim, Seong Joon Yo, Dongil Han, Hyoung Gon Kim,IEEE 2008, IEEE Conference On Intelligent System,Korea
Measuring the Availability of Images Posted on Social Media Sites, Arash Nourian, Muthucumaru Maheswaran, School Of Computer Science, Canada IEEE 2012
Ramin Zabih, Justin Miller, and Kevin Mai. A feature-based algorithm for detecting and classifying scene breaks. In ACM Multimedia Conference, pages 189-200, November 1995.
Rena Valova, Boris Rachev and Michael Vassilakopoulos, “Optimization of the Algorithm for Image Retrieval by Color Features”, International Conference on Computer Systems and Technologies CompSysTech‟, pp 1- 4, 2006
Rafel C. G nzalez and Richard E. Woods, “Digital Image Processing”, Second Edition, Pearson Education Asia, 2005 Aleksandra Mojsilovic, Jianying Hu and Emina Soljanin, “Extraction of Perceptually Important Colors and Similari.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.