In this paper, we analyzed the increment distribution and the self-similarity behavior of texture images of three kinds of
particular underwater objects during the mineral hunting process. The experimental data has shown that the H exponent
of real underwater natural texture is not a constant over all scale range, but a variable with respect to the measure scale or
time index. In order to investigate the multi-scale self-similarity behavior of the objects, we had extended the traditional
FBM so that the self-similarity parameter H is taken as a variable H(s) with respect to measure scale s. The class-separability
of self-similarity feature is measured, and the feature selection criterion is given. Pattern classification
simulation experimental results have shown the effectiveness of the selected feature set combining the self-similarity
parameter HΔ(3), the variance D(HΔ) and the increment variance AD. The correct ratio is up to 96% on average, which
can be used in automatic detection and recognition for AUVs to complete their tasks.
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