A Review on Image Retrieval with Its Application and Techniques
In the field of image processing, image retrieval is one of the most dynamic exploration field in the previous few years. In the image retrieval three main categories there are text-based, content-based and semantic-based. In content based image retrieval (CBIR) images are retrieved from their visual content such as color, texture and shapes. CBIR is used to effectively retrieve required images from a large collection of images. In this paper, the methods of CBIR are discussed, analyzed and compared. There are some different techniques for extracting color, texture and shape features. CBIR is a very important research area in the field of image processing, and comprises of low level feature extraction, such as color, texture and shape and similarity measures for the comparison of images. CBIR is the retrieving the images task from the huge database collection on the basis of the own visual content. Content ased Image Retrieval is used for image retrieval and automatic indexing depending upon the contents of images known as features. Various content-based image retrieval techniques are available for retrieving the required and classify images we are reviewing them.
Cite this Article
Gupta B, Shrivastava S. A Review on Image Retrieval with its Application and Techniques. Journal of Artificial Intelligence Research and Advances. 2015; 2(3): 27–35p.
Gao Y, Chan K, Yau W et al. Learning in Content Based Image Retrieval – A Brief Review. 6thInternational Conference on Information, Communications & Signal Processing. 2007; 1–5p.
Chang SK and Hsu A. Image information systems: where do we go from here? IEEE Trans. On Knowledge and Data Engineering. 1992; 5(5): 431–442p.
Gandhani S, Bhujade R, Sinhal A. An Improved and Efficient Implementation of CBIR Systems Based on Combined Features .
Rafiee G, Dlay SS, Woo WL et al. A Review Of Content Based Image Retrieval
Database, SCIEXPANDEDand Conference Proceedings Citation Index-Science (CPCI-S). 2004; 73(1): 1–23p
Shyuet CR et al. Local versus Global Features for Content- Based Image Retrieval. IEEE Workshop on Content-Based Access of Image and Video Libraries. Workshop onContent-Based Access of Image and Video Libraries. 1998.
Yong et al. Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval. IEEE Transactions On Circuits And Systems For VideoTechnology. 1998; 8(5).
Nandagopalan et al. A Universal Model for Content-Based Image Retrieval. International Journal of Electrical and Computer Engineering. 2009; 4(4): 249–252p.
Minkashi Banerjee. Elesvier, CBIR using visually significant point features, Fuzzy Sets and Systems. 2009; 160(23): 3323–3341p.
BabuRao et al. Content Based Image Retrieval using Dominant Color, Texture and Shape. International Journal of Engineering Science and Technology (IJEST). 2011; 3(4): 2887–2896p.
Hiremath PS and JagadeeshPujari. Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement. International Journal of Computer Science and Security. 2007; 1(4):
Gevers T, Smeulders AWM. Picto seek: Combining color and shape invariant features for image retrieval. IEEE Trans. on Image Processing. 2000; 9(1): 102–119p.
Huang J. Color-Spatial Image Indexing and Applications PhD Thesis. Cornell Univ. 1998.
Hsu W, Chua TS and Pung HK. An integrated color spatial approach to content-based image retrieval ACM Multimedia Conference. 1995; 305–313p.
Subitha S and Sujatha S. Survey Paper on Various Methods in Content Based Information Retrieval IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET). ISSN 2321-8843 Aug 2013; 1(3): 109–120p.
Vivek Jain, Neha Sahu. A Survey: On Content Based Image Retrieval. International Journal of Engineering Research and Applications. Jul-Aug 2013; 3(4): 1166–1169p.
Rajam FI and Valli S. A Survey on Content Based Image Retrieval. Life Science Journal. 2013; 10(2).
Yasmin M, Sharif M, Mohsin S. Use of Low Level Features for Content Based Image Retrieval. www.isca.in, www.isca.me, , Nov 2013; 2(11): 65–75p.
Panchal CK, Tiwari AR. A Survey on CBIR using Low Level Feature Combination. International Journal of Emerging Technology and Advanced Engineering. Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Nov 2014; 4(11).
Yadav AM, Sengar BPS. A Survey on Content Based Image Retrieval Systems. International Journal of Emerging Technology and Advanced Engineering. Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal. June 2014; 4(6).
Velmurugan. A Survey of Content-Based Image Retrieval Systems using Scale-Invariant Feature Transform (SIFT). www.ijarcsse.com. Jan 2014; 4(1).
Mistry Y, Ingole DT. Survey on Content Based Image Retrieval Systems. International Journal of Innovative Research in Computer and Communication Engineering. Oct 2013; 1(8): 1827–1836p.
Gunjal S, Rokade SM. A Survey over the Content-Based Image Retrieval Techniques. International Journal of Science and Research (IJSR). 2012.
Gupta E, Kushwah RS. Combination of Local, Global and k-mean using Wavelet Transform for Content Base Image Retrieval. International Journal of Signal Processing, Image Processing and Pattern Recognition. 2015; 8(6): 253–266p.
- There are currently no refbacks.