Shape Extraction Using Edge Detection Techniques

Content Based Image Retrieval (CBIR) is a system that searches for similar images from a large image database according to users' specification. Digital image database is growing rapidly in size as a result of technological advancements. Traditional systems like text based image retrieval faces too many problems like human perception, deeper needs, image annotation and etc. Thus it is difficult to support a variety of task-dependent queries for traditional systems. CBIR systems comprehend a wide areas, viz. image segmentation, image feature extraction, representation, feature matching, storage and indexing, image retrieval - making CBIR system development a challenging task. The visual content of an image is analyzed features (i.e., color, shape, texture) extracted from the image. CBIR system is used in Medical Imagery Retrieval, Finger Print Retrieval, Satellite Imagery Retrieval, Internet, and Photo Collections. The paper focuses upon shape based image retrieval. With the help of three different edge detection algorithms, they extract the shape features from a given input image.

[1]  Azriel Rosenfeld,et al.  Optimal edge-based shape detection , 2002, IEEE Trans. Image Process..

[2]  A. M. Patil,et al.  Content Based Image Retrieval Using Color and Shape Features , 2012 .

[3]  R. Maini.  Study and Comparison of Various Image Edge Detection Techniques , 2004 .

[4]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[5]  Ueda Kazuaki,et al.  Image Retrieval using Shape Features , 2006 .

[6]  P. S. Suhasini,et al.  CBIR USING COLOR HISTOGRAM PROCESSING , 2009 .

[7]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[8]  Ritika Hirwane.  Fundamental of Content Based Image Retrieval , 2012 .

[9]  Mohamed Ali,et al.  Using the Canny edge detector for feature extraction and enhancement of remote sensing images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[10]  K. Satya Prasad,et al.  Multiwavelet Based Texture Features for Content Based Image Retrieval , 2011 .

[11]  P. V. N. Reddy,et al.  Color and Texture Features for Content Based Image Retrieval , 2011 .

[12]  Fuhui Long,et al.  Fundamentals of Content-Based Image Retrieval , 2003 .

[13]  Shwu-Huey Yen,et al.  A study of shape-based image retrieval , 2004, 24th International Conference on Distributed Computing Systems Workshops, 2004. Proceedings..

[14]  Kulvinder Singh Mann.  An Approach of Image Retrieval Using Content Based Retrieval System , 2013 .

[15]  Bapurao Deshmukh,et al.  Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture , 2011 .

[16]  S. Arivazhagan,et al.  Image Retrieval using Shape Feature , 2007 .