Large image databases find diverse applications in real-life
situations. It is essential to develop an efficient technique to
grasp required information from these databases. Fast retrieval and
robustness are two main criteria for efficient recognition of an
object using image databases. Keeping this in mind, the present
research work describes a hybrid method using a weighted
combination of multiple image features mainly color histogram,
distance, and moment parameters for trademark recognition. The
computational power is accelerated by employing a low cost NVIDIA's
Compute Unified Device Architecture (CUDA) enabled GPU. Our
experimental results show that this new technique provides more
robust performance than either of the individual methods and our
GPU implementation requires less than 50% of the CPU computation
time. This experimentation demonstrates that the method based on
multiple weighted images features with cooperative implementation
between the CPU and GPU is an effective way of image retrieval from
large databases.
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