Abstract: Several computationally challenging issues are
encountered while classifying complex natural scenes. In this
paper, we address the problems that are encountered in rotation
invariance with multi-intensity analysis for multi-scene overlapping.
In the present literature, various algorithms proposed techniques
for multi-intensity analysis, but there are several restrictions in
these algorithms while deploying them in multi-scene overlapping
classifications. In order to resolve the problem of multi-scenery
overlapping classifications, we present a framework that is based
on macro and micro basis functions. This algorithm conquers the
minimum classification false alarm while pigeonholing multi-scene
overlapping. Furthermore, a quadrangle multi-intensity decay is
invoked. Several parameters are utilized to analyze invariance
for multi-scenery classifications such as rotation, classification,
correlation, contrast, homogeneity, and energy. Benchmark datasets
were collected for complex natural scenes and experimented for
the framework. The results depict that the framework achieves
a significant improvement on gray-level matrix of co-occurrence
features for overlapping in diverse degree of orientations while
pigeonholing multi-scene overlapping.