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Content-Based Image Retrieval involves searching a database of photographs for the pictures that look like the query picture. With the help of Feature Extraction Technique, number of required images can be extracted from the database based on the query image. Fuzzy Cmean, Kmean, Gray-Level Co-Occurrence Matrix, Local Binary Pattern, Principal Component Analysis and Scale Invariant Feature transform are used to extract features. The main motive of this work is to improve the efficiency of Content-Based Image Retrieval system by eliminating characteristics from query and database images. The Markov Chain Rule Feature Optimization approach is used to choose the informative features from the features. For similarity search, Hausdarff Distance along with Gradient Boosting machine learning techniques improves matching accuracy and retrieval speed. To compare to the state of the art, we have got 97.71% accuracy and they got 93.89% and 94.79%.

Markov chain latent space probabilistic for feature optimization along with Hausdorff distance similarity matching in content-based image retrieval