Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding

One of the biggest challenges in real world decision making process is to cope with uncertainty, complexity, volatility and ambiguity. How do we deal with this growing confusion in our world? In scene understanding, an important and yet difficult image understanding problem due to their variability, ambiguity, wide range of illumination and scale conditions falls into this category. The conventional goal of the works is to assign an unknown scene image to one of the several possible classes. For example, Figure 1(a) is a Coast class scene while Figure 1(c) is a Mountain class scene. Intentionally, most state-of-the-art approaches in scene understanding domain [1]-[4] are exemplar-based and assume that scene images are mutually exclusive, P (A B) 0. This simplifies the complex problem of scene understanding (uncertainty, complexity, volatility, and ambiguity) to a simple binary classification task. Such approaches learn patterns from a training set and subsequently, search for the images similar to it. As a result of this, classification errors often occur when the scene classes overlap in the selected feature space.