This paper presents the realization of area efficient architecture using Distributed Arithmetic with Offset Binary Coding (DA-OBC) for implementation of Finite Impulse Response (FIR) Filter. Area complexity in the algorithm of Finite Impulse Response Filter is mainly caused by multipliers. Among the multiplierless techniques of FIR Filter, Distributed Arithmetic is most preferred area efficient technique. In this technique, partial products of filter coefficients are precomputed and stored in Lookup Table (LUT) and the filtering is done by shift and accumulate operations on these partial products. However, the scale of the LUT will increase exponentially with the coefficient. If the coefficient is small, it is very convenient to realize. While the coefficient is large, it will take up a lot of storage resources of FPGA and reduce the calculation speed. The paper presents the improvement of the DA algorithm by reducing the LUT size and delay using Offset Binary Coding Algorithm. The design based on Altera EP2S15F484C3 Chips is synthesized under the integrated environment of Quartus II 9.1. The result of simulation and test shows that Offset Binary Coding greatly reduces the FPGA hardware resources and the high speed filtering is achieved compared to conventional DA Algorithm.
Automatic image annotation is an efficient and promising solution in content based image retrieval system applications to process very large databases via keywords. The basic idea of semantic modeling is to describe local image regions into semantic concepts using low level features such as color and texture. These local image region descriptions are combined to a global image representation that can be used for scene categorization and retrieval. In this paper, Local Binary Pattern features and neighborhood prior information are used as texture and spatial features for local image representation that allows access to natural scenes. K-Means classifier has been used to support automatic image annotation of local image region into semantic classes such as water, sky, and trees. Extensive experiments on databases like COREL, shows that the proposed technique performs well in scene classification.
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