Budanitsky, A., &Hirst, G. (2001, June). Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Workshop on WordNet and other lexical resources (Vol. 2, pp. 2-2).
Cao, X., & Wang, S. (2012). Research about image mining technique. In Communications and Information Processing (pp. 127-134). Springer, Berlin, Heidelberg.
El-Alami, M. E. (2014). A new matching strategy for content based image retrieval system. Applied Soft Computing, 14, 407-418.
Franzoni, V., Leung, C. H., Li, Y., Mengoni, P., &Milani, A. (2015, June). Set similarity measures for images based on collective knowledge. In International Conference on Computational Science and Its Applications (pp. 408-417). Springer, Cham.
Finlayson, M. (2014, January). Java libraries for accessing the princetonwordnet: Comparison and evaluation. In Proceedings of the Seventh Global Wordnet Conference (pp. 78-85).
Franzoni, V., Milani, A., Pallottelli, S., Leung, C. H., & Li, Y. (2015, August). Context-based image semantic similarity. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 1280-1284). IEEE.
G. Mont´ufar, R. Pascanu, K. Cho, and Y. Bengio, (2014), On the number of linear regions of deep neural networks. In NIPS.
Girshick., R. (2015), Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448.
Goel, N., &Sehgal, P. (2013, August). Weighted semantic fusion of text and content for image retrieval. In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 681-687). IEEE.
Goel, N., & Sehgal, P. (2014). Image Retrieval Using Fuzzy Color Histogram and Fuzzy String Matching: A Correlation-Based Scheme to Reduce the Semantic Gap. In Intelligent Computing, Networking, and Informatics (pp. 327-341). Springer, New Delhi.
He., K and Sun., J. 2015 Convolutional neural networks at constrained time cost. In CVPR.
Huang, Z. C., Chan, P. P., Ng, W. W., & Yeung, D. S. (2010, July). Content-based image retrieval using color moment and Gabor texture feature. In 2010 International conference on machine learning and cybernetics (Vol. 2, pp. 719-724). IEEE.
Li, X., Uricchio, T., Ballan, L., Bertini, M., Snoek, C. G., & Bimbo, A. D. (2016). Socializing the semantic gap: A comparative survey on image tag assignment, refinement, and retrieval. ACM Computing Surveys (CSUR), 49(1), 1-39.
Lin, C. H., Chen, R. T., & Chan, Y. K. (2009). A smart content-based image retrieval system based on color and texture feature. Image and Vision Computing, 27(6), 658-665.
M. D. Zeiler and R. Fergus (2014). Visualizing and understanding convolutional neural networks. In ECCV.
MATLAB and Statistics Toolbox Release 2013a, TheMathWorks, Inc., Natic, Massachusetts, United States.
Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41.
Murala, S., Gonde, A. B., &Maheshwari, R. P. (2009, March). Color and texture features for image indexing and retrieval. In 2009 IEEE International Advance Computing Conference (pp. 1411-1416). IEEE.
Neelima, N., & Reddy, E. S. (2016). An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 257-265). Springer, Cham.
Oliva, A., &Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision, 42(3), 145-175.
R. K. Srivastava, K. Greff, and J. Schmidhuber (2015), Training very deep networks. 1507.06228.
Singh, K., Singh, K. J., & Kapoor, D. S. (2014, September). Image Retrieval for Medical Imaging Using Combined Feature Fuzzy Approach. In 2014 International Conference on Devices, Circuits and Communications (ICDCCom) (pp. 1-5). IEEE.
Wu, L., Hua, X. S., Yu, N., Ma, W. Y., & Li, S. (2011). Flickr distance: a relationship measure for visual concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(5), 863-875.
Zarchi, M. S., Monadjemi, A., &Jamshidi, K. (2015). A concept-based model for image retrieval systems. Computers & Electrical Engineering, 46, 303-313.