Content-Based Representation For Moth Recognition And Retrieval: A Review
About this article
DOI:
https://doi.org/10.14419/ijet.v7i4.38.27909Keywords:
Bag of Visual Words, moth, recognition, spatial information, retrievalAbstract
There are numerous of moths on the earth and their presence is useful to our life especially as life indicators. Most of the entomologists have problems to recognize moth’s species because each of them has their own color, texture and shape. The varieties of color, texture and shape of moths has increased the researcher’s attention to improve the method in recognizing the moth’s species. This study investigates the effectiveness of Bag of Visual Words (BOVW) representation for recognizing the moth’s species. BOVW is a simple and effective representation. This representation broadly used especially in computer vision and object recognition. Local descriptors, clustering approaches, and word-image histograms in regards to BOVW for image classification and retrieval are studied. There is a contradiction between BOVW models about spatial information. The extension of BOVW to consider the spatial information is believed able to contribute to a more effective representation for moth recognition and retrieval.
References
Ali, N., Bajwa, K. B., Sablatnig, R. & Mehmood, Z. (2016). Image Retrieval by Addition of Spatial Information Based on Histogram Of Triangular Regions. Computer and Electrical Engineering, 54, 539-550. http://dx.doi.org/10.1016/j.compeleceng.2016.04.002
Anwar, H., Zambanini, S. & Kampel, M. (2015). Efficient Scale-and Rotation-Invariant Encoding of Visual Words for Image Classification. IEE Signal Processing Letters, 22(10). doi:10.1109/LSP.2015.2432851
Csurka, G., Dance, C. R., Fan, L., Willamowski, J. & Bray, C. (2004). Visual Categorization with Bags of Keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV. (pp. 1–22).
Farhangi, M. M., Soryani, M., & Fathy, M. (2013). Informative Visual Words Construction to Improve Bag of Words Image Representation. IET Image Processing, 8(5), 310-318. doi:10.1049/iet-ipr.2013.0449
Franco, A., Maltoni, D. & Papi, S. (2017). Grocery Product Detection and Recognition. Expert System with Application, 81, 163-176. http://dx.doi.org/10.1016/j.eswa.2017.02.050
View more references (17)
Feng, L., Bhanu, B. & Heraty, J. (2016). A Software System for Automated Identification and Retrieval of Moth Images Based on Wing Attributes. Pattern Recognition. 51, 225-241. http://dx.doi.org/10.1016/j.patcog.2015.09.012
Ghosh, M., Mukherjee, J. & Parekh, R. (2013). Fish Shape Recognition Using Multiple Shape Descriptors. International Journal of Computer Applications, 73(16).
Jiang, Y. -G., Yang, J., Ngo, C. -W. & Hauptmann. A. G. (2010). Representation of Keypoint- based Semantic Concept Detection: A Comprehensive Study. IEEE Transactions on Multimedia, 12(1). doi:10.1109/TMM.2009.2036235
Khan, R., Barat, C., Muselet, D. & Ducottet, C. (2015). Spatial Histogram of Soft Pairwise Similar Patcher to Improve the Bag- of- Visual- Words Model. Computer Vision and Image Understanding, 132, 102-112. http://dx.doi.org/10.1016/j.cviu.2014.09.005
López-Monroy, A. P., Montes-y-Gómez, M., Escalante, H. J., Cruz-Roa, A., & González, F. A. (2016). Improving The Bovw Via Discriminative Visual N-grams and MKL Strategies. Neurocomputing, 175, 768-781. http://dx.doi.org/10.1016/j.neucom.2015.10.053
Mansourian, L., Abdullah, M. T., Abdullah, L. N., Azman, A., & Mustaffa, M. R. (2016). BoVW Model for Animal Recognition: An Evaluation on Sift Feature Strategies. In Badioze Zaman, H., Robinson, P., Smeaton, A. F., Shih, T. K., Velastin, S., Jaafar, A., & Mohamad Ali, N. (Eds.). Advances in Visual Informatics (pp. 227-236). Heidelberg: Springer International Publishing.
Osman, N. S. & Mustaffa, M. R. (2015). A Review on Content-Based Image Retrieval Representation and Description for Fish. Proceedings of the 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2015), 8-10 December 2015, Kuala Lumpur, Malaysia (pp. 39-44). IEEE.
Pires, R. D. L. et al. (2016). Local Descriptor for Soybean Disease Recognition. Computer and Electronic in Agriculture, 125, 48-55. http://dx.doi.org/10.1016/j.compag.2016.04.032
Sheikh, A. R., Lye, M. H., Mansor, S., & Fauzi, M. F. A. (2011). A Content Based Image Retrieval System for Marine Life Images. 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE), 29–33. http://doi.org/10.1109/ISCE.2011.5973777
Sivic, J. & Zisserman, A. (2003). Video Google: A Text Retrieval Approach to Object Matching in Videos. Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV’03). IEEE.
Tu, J., Sui, H., Feng, W., Sun, K., & Hua, L. (2016). Detection of Damaged Rooftop Areas from High-Resolution Aerial Images Based on Visual Bag-of Words Model. IEEE Geoscience and Remote Sensing Letters, 13(12). doi:10.1109/LGRS.2016.2614298
Wang, R., Ding, K., Yang, J. & Xue, L. (2016). A Novel Method for Image Classification Based on Bag of Visual Words. J. Vis. Commun. Image R., 40, 24-33. http://dx.doi.org/10.1016/j.jvcir.2016.05.022
Wen, C., Zhou, X., Zhou, C., Chen, X. & Guo, Q. (2013). Bag of Visual Words for Cows’ Basic Activity Recognition. 2013 International Conference on Information Science and Cloud Computing.
Wang, J., Ji, L., Liang, A. & Yuan, D. (2012). The Identification of Butterfly Families Using Content Based Image Retrieval. Biosystem Engineering III. 24-32. doi:10.1016/j.biosystemseng.2011.10.003
Wen, C., Guyer, D. E. & Li, W. (2009). Local Feature-Based Identification and Classification for Orchard Insects. Biosystem Engineering, 104, 299-307. doi:10.1016/j.biosystemseng.2009.07.002
Zhang, F., et al. (2016). Dictionary Pruning with Visual Word Significance for Medical Image Retrieval. Neurocomputing, 177, 75-88. http://dx.doi.org/10.1016/j.neucom.2015.11.008
Zhu, C., & Peng, Y. (2017). Discriminative Latent Semantic Feature Learning for Pedestrian Detection. Neurocomputing, 238, 126-138. http://dx.doi.org/10.1016/j.neucom.2017.01.043