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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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. We present an approach to jointly estimating camera motion and dense structure of a static scene in terms of depth maps from monocular image sequences in driver-assistance scenarios. At each instant of time, only two consecutive frames are processed as input data of a joint estimator that fully exploits second-order information of the corresponding optimization problem and effectively copes with the non-convexity due to both the imaging geometry and the manifold of motion parameters. Additionally, carefully designed Gaussian approximations enable probabilistic inference based on locally varying confidence and globally varying sensitivity due to the epipolar geometry, with respect to the high-dimensional depth map estimation. Embedding the resulting joint estimator in an online recursive framework achieves a pronounced spatio-temporal filtering effect and robustness. We evaluate hundreds of images taken from a car moving at speed up to 100 km h and being part of a publicly available benchmark data set. The results compare favorably with two alternative settings: stereo based scene reconstruction and camera motion estimation in batch mode using multiple frames. They, however, require a calibrated camera pair or storage for more than two frames, which is less attractive from a technical viewpoint than the proposed monocular and recursive approach. In addition to real data, a synthetic sequence is considered which provides reliable ground truth. Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top–down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. We present a novel probabilistic approach for fitting a statistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined using a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable detections and therefore does not need the detectors to take an early and possible wrong hard decision before fitting. Second, it is open for integration of various cues to guide the fitting process. Based on the proposed approach, we implemented a completely automatic, pose and illumination invariant face recognition application. We are able to train and test the building blocks of our application on different databases. The system is evaluated on the Multi-PIE database and reaches state of the art performance.