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Busemeyer, L.; Mentrup, D.; Möller, K.; Wunder, E.; Alheit, K.; Hahn, V.; Maurer, H.P.; Reif, J.C.; Würschum, T.; Müller, J.; et al. 13. Ahonen, T.; Matas, J.; He, C.; Pietikainen, M. Rotation invariant image description with native binary sample histogram fourier features. 5. Datta, R.; Joshi, D.; Li, J.; Wang, J.Z. 28. Johnson, R.; Zhang, T. Semi-supervised Convolutional Neural Networks for Text Categorization by way of Region Embedding. 24. Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for giant-Scale Image Recognition. 26. Tzelepi, M.; Tefas, A. Deep convolutional studying for Content Based Image Retrieval. Click on Duplicate Content to see the individual URLs. For better reference see here.. Python bassed. See additionally the Zope Application Server evaluate article for an impartial review/overview. 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Abdel-Hamid, O.; Mohamed, A.R.; Jiang, H.; Deng, L.; Penn, G.; Yu, D. Convolutional Neural Networks for Speech Recognition. 32. Tzelepi, M.; Tefas, A. Deep convolutional picture retrieval: A general framework. Image retrieval: Ideas, influences, and tendencies of the brand new age. 21. Perronnin, F.; Liu, Y.; Sánchez, J. Large-scale image retrieval with compressed fisher vectors. 25. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going Deeper with Convolutions. 1. Virlet, N.; Sabermanesh, K.; Sadeghi-Tehran, P.; Hawkesford, M.J. 3. Kirchgessner, N.; Liebisch, F.; Yu, K.; Pfeifer, J.; Friedli, M.; Hund, A.; Walter, A. The ETH discipline phenotyping platform FIP: A cable-suspended multi-sensor system. 6. Lew, M.; Sebe, N.; Djeraba, C.; Jain, R. Content-primarily based multimedia data retrieval: State of the art and challenges. 4. Larson, R.R. Introduction to Information Retrieval. A sensible content material-primarily based picture retrieval system based on shade and texture feature. 11. Singh, S.M.; Hemach, K.; Hemachandran, K. Content-Based Image Retrieval using Color Moment and Gabor Texture Feature
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