Abstract
This paper describes a method for image classification and retrieval for natural and urban scenes. The proposed algorithm is based on hierarchical image contents analysis. First image is classified as urban or natural according to color and edge distribution properties. Additionally scene is classified according to its conditions: illumination, weather, season and daytime based on contrast, saturation and color properties of the image. Then image content is analyzed in order to detect specific object classes: buildings, cars, trees, sky, road etc. To do so, image recursively divided into rectangular blocks. For each block probabilities of membership in the specific class is computed. This probability computed as a distance in a feature space defined by optimal feature subset selected on the training step. Blocks which can not be assigned to any class using computed features are separated into 4 sub-blocks which analyzed recursively. Process stopped then all blocks are classified or size of block is smaller then predefined value. Training process is used to select optimal feature subset for object classification. Training set contains images with manually labeled objects of different classes. Each image additionally tagged with scene parameters (illumination, weather etc).
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Adams, W.H., Iyengar, G., Lin, C., Naphade, M.R., Neti, C., Nock, H.J., Smith, J.R.: Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues. Journal on Applied Signal Processing 2, 1–16 (2003)
Wang, J., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)
Chang, E., Goh, K., Sychay, G., Wu, G.: Cbsa: Content-based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines. IEEE Transactions on Circuits and Systems for Video Technology Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description 13(1), 26–38 (2003)
Shen, J., Shepherd, J., Ngu, A.H.H.: Semantic-sensitive Classification for Large Image Libraries. In: International Multimedia Modelling Conference, Melbourne, Australia, pp. 340–345 (2005)
Paek, S., Chang, S.F.: A Knowledge Engineering Approach for Image Classification Based on Probabilistic Reasoning Systems. In: IEEE International Conference on Multimedia and Expo., New York, vol. II, pp. 1133–1136 (2000)
Serrano, N., Savakis, A., Luo, J.: Improved Scene Classification Using Efficient Low-level Features and Semantic Cues. Pattern Recognition 37, 1773–1784 (2004)
Shandilya, S.K., Singhai, N.: A Survey On: Content Based Image Retrieval Systems. International Journal of Computer Applications 4(2), 22–26 (2010)
Bosch, A., Muñoz, X., MartÃ, R.: Review: Which Is the Best Way to Organize/Classify Images by Content? Image and Vision Computing 25(6), 778–791 (2007)
Vavilin,A., Ha,L.M., Jo,K.H.: Optimal Feature Subset Selection for Urban Scenes Understanding. In: URAI 2010 Busan, Korea (2010)
Torralba, Q.A.: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision 42(3), 145–175 (2001)
Oliva, A., Torralba, A.: Scene-centered Description from Spatial Envelope Properties. In: BMCV 2002. LNCS, vol. 2525, pp. 263–272. Springer, Heidelberg (2002)
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Vavilin, A., Jo, KH., Jeong, MH., Ha, JE., Kang, DJ. (2011). Automatic Context Analysis for Image Classification and Retrieval. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_51
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DOI: https://doi.org/10.1007/978-3-642-24728-6_51
Publisher Name: Springer, Berlin, Heidelberg
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