Abstract
We tackle the problem of retrieving images from a database. In particular we are concerned with the problem of retrieving images of airplanes belonging to one of the following six categories: 1) commercial planes on land, 2) commercial planes in the air, 3) war planes on land, 4) war planes in the air, 5) small aircrafts on land, and 6) small aircrafts in the air. During training, a wavelet-based description of each image is first obtained using Daubechies 4-wavelet transformation. The resulting coefficients are then used to train a neural network. During classification, test images are presented to the trained system. The coefficients are obtained from the Daubechies transform from histograms of a decomposition of the image into square sub-images of each channel of the original image. 120 images were used for training and 240 for independent testing. An 88% correct identification rate was obtained.
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References
Del Bimbo, A.: Visual Information Retrieval. Morgan Kaufmann, San Francisco (1999)
Lew, S.M.: Next-Generation Web Searches for Visual Content. Computer, Information Retrieval 33(11), 46–53 (2000)
Leung, C.: Visual Information Systems. Springer, Heidelberg (1997)
Park, S.B., Lee, J.W., Kim, S.K.: Content-based image classification using a neural network. Pattern Recognition Letters 25, 287–300 (2004)
Zhang, S., Salari, E.: Image denoising using a neural network based on non-linear filter in wavelet domain, Acoustics, Speech, and Signal Processing. In: Proceedings IEEE International Conference ICASSP 2005, March 18-23, 2005, vol. 2, pp. 989–992 (2005)
Manish, N.S., Bodruzzaman, M., Malkani, M.J.: Feature Extraction using Wavelet Transform for Neural Network based Image Classification. IEEE, Los Alamitos (1998), 0-7803-4547-9
Puzicha, J., Hofmann, T., Buhmann, J.M.: Histogram Clustering for Unsupervised Segmentation and Image Retrieval. Pattern Recognition Letters 20, 899–909 (1999)
Mandal, M.K., Aboulnasr, T.: Fast Wavelets Histogram Techniques for Image Indexing. Computer Vision and Understanding 75(1-2), 99–110 (1999)
Liapis, S., Tziritas, G.: Color and texture image retrieval using chromaticity histograms and wavelet frames. Multimedia, IEEE Transactions on Multimedia (5), 676–686 (2004)
Vetterli, M.: Wavelets, Approximation, and Compression. IEEE Signal Processing Magazine 1, 59–73 (2000)
Daubechies, I.: Orthonormal bases of compactly supported wavelets. Comm. Pure and Applied Mathematics. 41, 909–996 (1988)
Xiong, Z., Ramchandran, K., Orchard, M.T.: Space-Frequency Quantization for Wavelet Image Coding. IEEE Transactions on Image Processing 6(5) (1997)
Papamarcos, N., Atsalakis, A.E., Strouthopoulos, C.P.: Adaptive Color Reduction. IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics 32(1) (2002)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab. Pearson Prentice Hall (2004)
Jou, F.D., Fan, K.C., Chang, Y.L.: Efficient matching of large-size histograms. Pattern Recognition Letters 25, 277–286 (2004)
McGuire, P., D‘Eleuterio, G.M.T.: Eigenpaxels and a Neural Network Approach to Image Classification. IEEE Transactions on Neural Networks 12(3) (2001)
Gasca, A.E., Barandela, A.R.: Algoritmos de aprendizaje y técnicas estadísticas para el entrenamiento del Perceptrón Multicapa. IV Simposio Iberoamericano de Reconocimiento de Patrones, Cuba, pp. 456-464 (1999)
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Gonzalez, A.C., Sossa, J.H., Riveron, E.M.F., Pogrebnyak, O. (2006). Histograms, Wavelets and Neural Networks Applied to Image Retrieval. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_78
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DOI: https://doi.org/10.1007/11925231_78
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