Ambiguity or uncertainty is a pervasive element of many real-world decision-making processes. Variation in decisions is a norm in this situation when the same problem is posed to different subjects. Psychological and metaphysical research has proven that decision making by humans is subjective. It is influenced by many factors such as experience, age, background, etc. Scene understanding is one of the computer vision problems that fall into this category. Conventional methods relax this problem by assuming that scene images are mutually exclusive; therefore, they focus on developing different approaches to perform the binary classification tasks. In this paper, we show that scene images are nonmutually exclusive and propose the fuzzy qualitative rank classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC provides a ranking interpretation instead of binary decision. Evaluations in terms of qualitative and quantitative measurements using large numbers and challenging public scene datasets have shown the effectiveness of our proposed method in modeling the nonmutually exclusive scene images.
In what constitutes the closer work to ours in the fuzzy domain, Lim and Chan @cite_3 proposed a fuzzy qualitative framework and Cho and Chang @cite_17 employed a simple fuzzy logic with two monocular images to understand the scene images. However, their work suffered from 1) finding the appropriate resolution to build their 4-tuple membership function. Currently, the model parameters are chosen manually based on prior information and in a trial-and-error manner. This is a very tedious and time consuming approach; 2) only able to accommodate two feature vectors as input data; 3) the ranking is undefined and finally 4) tested on a very limited and easy dataset (a dataset that contains only 2 scene images).
@cite_3 @cite_17
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Scene classification has been studied extensively in the recent past. Most of the state-of-the-art solutions assumed that scene classes are mutually exclusive. However, this is not true as a scene image may belongs to multiple classes and different people are tend to respond inconsistently even given a same scene image. In this paper, we propose a fuzzy qualitative approach to address this problem. That is, we first adopted the fuzzy quantity space to model the training data. Secondly, we present a novel weight function, w to train a fuzzy qualitative scene model in the fuzzy qualitative states. Finally, we introduce fuzzy qualitative partition to perform the scene classification. Empirical results using a standard dataset and a comparison with K-nearest neighbour has shown the effectiveness and robustness of the proposed method. This paper proposes a two-stage scene analysis scheme using a combined fuzzy logic-based technique. The first stage begins with generating fuzzy rules to describe the scene. Based on these fuzzy classification rules, each image pixel is inferred and then classified to the natural object category with the largest membership degree. The second stage involves a newly derived fuzzy K-nearest neighbor algorithm that further refines the classification result obtained. With this second stage, the proposed system is robust because it is demonstrated to be insensitive to the variations of membership functions and image noise contamination. Simulations of real world images have shown that the proposed scheme is very successful and the results are visually confirmed by human observation. The satisfactory results achieved in this paper suggest the feasibility of developing similar systems for other types of images aiming at image description problems.