Survey paper on Sketch Based and Content Based Image Retrieval

This survey paper presents an overview of development of Sketch Based Image Retrieval (SBIR) and Content based image retrieval (CBIR) in the past few years. There is awful growth in bulk of images as well as the far-flung application in too many fields. The main attributes to represent as well index the images are color, shape, texture, spatial layout. These features of images are extracted to check similarity among the images. Generation of special query is the main problem of content based image retrieval. Features are extracted from the images and the similarity between them is check by using different algorithms. Sketch based image retrieval is effective and chief method which are not necessarily having a high skill to draw the query sketch. Over last twenty years different algorithms and models are explored for the retrieval of images. In concluding section we present the limitations of the current image retrieval algorithms.

[1]  Trevor Darrell,et al.  Beyond spatial pyramids: Receptive field learning for pooled image features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Veena Bansal,et al.  PATSEEK: Content Based Image Retrieval System for Patent Database , 2004, ICEB.

[3]  Eugenio Di Sciascio,et al.  Query by Sketch and Relevance Feedback for Content-Based Image Retrieval over the Web , 1999, J. Vis. Lang. Comput..

[4]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[5]  Neamat El Gayar,et al.  A new approach in content-based image retrieval using fuzzy , 2009, Telecommun. Syst..

[6]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[7]  Dragutin Petkovic,et al.  The query by image content (QBIC) system , 1995, SIGMOD '95.

[8]  Rui Hu,et al.  Gradient field descriptor for sketch based retrieval and localization , 2010, 2010 IEEE International Conference on Image Processing.

[9]  Narendra Ahuja,et al.  Learning subcategory relevances for category recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  Gyorgy Gyorok.  Embedded hybrid controller with programmable analog circuit , 2010, INES 2010.

[12]  B. S. Adiga,et al.  A Universal Model for Content-Based Image Retrieval , 2008 .

[13]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Marc Alexa,et al.  A descriptor for large scale image retrieval based on sketched feature lines , 2009, SBIM '09.

[15]  Z. Vamossy,et al.  Sketch4match — Content-based image retrieval system using sketches , 2011, 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[16]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Alexei A. Efros,et al.  Using Multiple Segmentations to Discover Objects and their Extent in Image Collections , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Chih-Chin Lai,et al.  A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm , 2011, IEEE Transactions on Instrumentation and Measurement.

[19]  Sudeep D. Thepade,et al.  Color Traits Transfer to Grayscale Images , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[20]  Jitendra Malik,et al.  Recognition using regions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Vikram Pudi,et al.  FISH: a practical system for fast interactive image search in huge databases , 2008, CIVR '08.

[22]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[23]  Jing-Ming Guo,et al.  Improved Block Truncation Coding Based on the Void-and-Cluster Dithering Approach , 2009, IEEE Transactions on Image Processing.

[24]  Samy Ait-Aoudia,et al.  YACBIR: Yet Another Content Based Image Retrieval System , 2010, 2010 14th International Conference Information Visualisation.

[25]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[26]  N. Krishnan,et al.  Content Based Image Retrieval Using Dominant Color Identification Based on Foreground Objects , 2007 .

[27]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[28]  Jilin Li,et al.  Local patterns constrained image histograms for image retrieval , 2008, 2008 15th IEEE International Conference on Image Processing.

[29]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[30]  Marc Alexa,et al.  An evaluation of descriptors for large-scale image retrieval from sketched feature lines , 2010, Comput. Graph..

[31]  N. Krishnan,et al.  Content Based Image Retrieval Using Dominant Color Identification Based on Foreground Objects , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[32]  Ling Guan,et al.  Computational Intellegence Techniques and their Applications in Content-Based Image Retrieval , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[33]  Chunguang Zhou,et al.  Image retrieval using multi-granularity color features , 2008, 2008 International Conference on Audio, Language and Image Processing.

[34]  Marc Alexa,et al.  Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors , 2011, IEEE Transactions on Visualization and Computer Graphics.

[35]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[36]  William I. Grosky.  Image Retrieval , 2008, Encyclopedia of Multimedia.

[37]  Xiangyang Wang,et al.  An effective image retrieval scheme using color, texture and shape features , 2011, Comput. Stand. Interfaces.