Ten lectures on wavelets
WALRUS: a similarity retrieval algorithm for image databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Comparing images using joint histograms
Multimedia Systems - Special issue on video content based retrieval
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian detection by means of far-infrared stereo vision
Computer Vision and Image Understanding
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Classification of binary random patterns
IEEE Transactions on Information Theory
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Hi-index | 12.05 |
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.