%0 Journal Article %1 noauthororeditor2013digital %A Saberi, Mohammad %A Tajeripour, Farshad %A Fekri-Ershad, Shervan %D 2013 %I AIRCC %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) %K Cropping Digital Image Local Object Texture TopHat Transform analysis binary pattern retrieval variance %N 1 %P 13 %R 10.5121/ijscai.2013.2102 %T Digital Image Retrieval Using Combination of Texture, Shape and Colour Features %U http://airccse.org/journal/ijscai/papers/0213scai02.pdf %V 2 %X A robust method is proposed in this paper to retrieve digital images using combination of color, texture and shape features. The proposed method consists of three stages. Shape detection is done based on TopHat transform to detect and crop object part of the image in the first stage. The second is included a texture feature extraction algorithm using color local binary patterns (CLBP) and local variance features together. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used as non-similarity ratio. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Shift invariant, Low noise sensitivity, rotation invariant are some of other advantages.

@article{noauthororeditor2013digital, abstract = {A robust method is proposed in this paper to retrieve digital images using combination of color, texture and shape features. The proposed method consists of three stages. Shape detection is done based on TopHat transform to detect and crop object part of the image in the first stage. The second is included a texture feature extraction algorithm using color local binary patterns (CLBP) and local variance features together. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used as non-similarity ratio. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Shift invariant, Low noise sensitivity, rotation invariant are some of other advantages. }, added-at = {2018-02-13T05:28:23.000+0100}, author = {Saberi, Mohammad and Tajeripour, Farshad and Fekri-Ershad, Shervan}, biburl = {https://www.bibsonomy.org/bibtex/21b602273b037154f57735716d4c49afa/leninsha}, doi = {10.5121/ijscai.2013.2102}, ee = {https://doi.org/10.1007/978-3-642-15231-3_32}, interhash = {240e79d4c8725c1b9290be0a079e46df}, intrahash = {1b602273b037154f57735716d4c49afa}, issn = {2319 - 1015}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)}, keywords = {Cropping Digital Image Local Object Texture TopHat Transform analysis binary pattern retrieval variance}, language = {English}, month = {February}, number = 1, pages = 13, publisher = {AIRCC}, timestamp = {2018-02-13T05:28:23.000+0100}, title = {Digital Image Retrieval Using Combination of Texture, Shape and Colour Features }, url = {http://airccse.org/journal/ijscai/papers/0213scai02.pdf}, volume = 2, year = 2013 }