Fast Similarity Search with Blocking Wavelet-Histogram and Adaptive Particle Swarm Optimization

The most existing systems of CBIR fulfill the tasks of retrieving the similar images through computing the degree of similarity of different images. Furthermore, the quality of the outcomes provided by color histogram-based image retrieval is usually rather limited. In this paper, a new image retrieval method is presented which is integrated the blocking wavelet-histogram with particle swam optimization (PSO). The innovative approach is used as solution to the problem of intelligent retrieval of images in large image databases. The problem is recast to a discrete optimization one, where a suitable speed and position of particle is defined through a customized PSO. Farther on, in virtue of the new computation model, a fitness function which combines blocking wavelet transformation information and the Euclidean distance of color histogram is constructed. The innovative approach based on blocking wavelet-histogram image similarity retrieval and particle swam optimization (PSO) is used for testing, and the experimental results show that our method is feasible and effective to image retrieval.