Multi-resolution semantic-based imagery retrieval using hidden Markov models and decision trees

This study presents a useful method for semantic-based imagery retrieval. The experiments are made in two parts. In the first part of the experiments, the newly designed one-dimensional hidden Markov models (HMM) in terms of ‘observation-sequence’ and ‘observation-density’ manipulation approaches are proposed so as to evaluate the corresponding performance in imagery retrieval accuracy. For the ‘observation-sequence’ manipulation method, there are totally four neighborhood systems being evaluated, while two neighborhood systems are tested in the ‘observation density’ manipulation domain. In the second part of the experiments, a C4.5 decision tree is introduced and trained by the HMM likelihoods so as to discover the retrieving rules to further enhance the imagery retrieval accuracy. The test imagery all belong to real-scene military vehicles and are hierarchically pre-processed using wavelet and LAB transforms. The imagery are classified into ‘Air-Force’, ‘Warship’, ‘Submarine’, ‘Tank’, and ‘Jeep’, respectively. It is found that using HMM alone can achieve the best accuracy of 68.8%, when decision trees are implemented, the accuracy can be further enhanced up to 78%. The results evidentially show the usefulness of the method, and can be used in intelligent systems in recognizing real-scene objects.