Introducing a hybrid framework for content-based image retrieval based on fractal dimension analysis and deep Boltzmann machine

The massive volume of images produced in recent years has made image retrieval one of the topics of research in the field of machine vision and image processing. The main challenge of content-based image retrieval systems is to extract the appropriate feature vector for image description to enable image retrieval effectively. In this research, a content-based image retrieval framework is introduced. The introduced feature vector is a combination of low-level features and mid-level features of the image. Extraction of low-level features of the image, including color, shape and texture, was performed using multi-level autocorrelation, discrete wavelet transform and fractal dimension analysis. Mid-level features are also extracted using the deep Boltzmann machine and by learning the low-level features of the image. The resulting feature vector is adjusted with 1K Corel database images and the performance of the proposed framework is also measured on 5K and 10K Corel databases. The best evaluation results are reported on 99.5%, 99.2% and 99.6% of the mentioned databases, respectively.