By Li Yan, Zongmin Ma
As buyer expenditures for multimedia units resembling electronic cameras and net telephones have lowered and variety out there has skyrocketed, the volume of electronic details has grown considerably.
Intelligent Multimedia Databases and data Retrieval: Advancing functions and Technologies info the newest details retrieval applied sciences and purposes, the examine surrounding the sphere, and the methodologies and layout concerning multimedia databases. including educational researchers and builders from either details retrieval and synthetic intelligence fields, this ebook info matters and semantics of knowledge retrieval with contributions from all over the world. because the details and information from multimedia databases maintains to extend, the learn and documentation surrounding it may retain velocity as most sensible as attainable, and this e-book presents a very good source for the most recent advancements.
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Additional resources for Intelligent multimedia databases and information retrieval: advancing applications and technologies
Zayane, O. , & Tauber, Z. (1999). Illumination invariance and object model in contentbased image and video retrieval. Journal of Visual Communication and Image Representation, 10, 219–244. 0403 Manjunath, B. , Ohm, J. , Vasudevan, V. , & Yamada, A. (2001). Color and texture descriptors. Transactions on Circuits and Systems for Video Technology, 11(6), 703–715. , & Kumar, S. R. (1997). Combining supervised learning with color correlograms for content-based image retrieval. Proceedings of Fifth ACM Multimedia Conference.
Performance Evaluation Approach To analyze the performance of system using the texture and shape features, retrieval precision and recall are introduced (Zhang et al, 2007). Suppose that N denotes the number of retrieved images at a time, N′ denotes the number of retrieved relevant images at a time, N′′ denotes the number of relevant images in an image database, then retrieval precision P and recall R can be denoted as respectively P = N′ / N and R = N′ / N′′ (21) EXPERIMENTS AND ANALYSES Works are carried out from three aspects.
Suppose that f (x, y) denotes a gray image of size M × N. After having been transformed into 34 The first Fourier transform coefficient a0 is used to normalize all the Fourier transform coefficients. Then the phase information is ignored and magnitudes are kept in the coefficients. Suppose that bn denotes the normalized Fourier transform coefficients whose phase information has been ignored, then bn can be denoted as bn = | an / a0 | (11) Approach of Using Texture and Shape for Image Retrieval Here bn (n = 1, …, PN-1) are invariant to translation, scale, rotation, and change of initial point.