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.
Oliva and Torralba @cite_23 proposed a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represents the dominant spatial structure of a scene - the spatial envelope as scene representation. Then, a support vector machine (SVM) classifier with Gaussian kernel is employed to classify the scene classes. Fei-Fei and Perona @cite_34 proposed the Bayesian hierarchical model extended from latent dirichlet allocation (LDA) to learn natural scene categories. In their learning model, they represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning, finally they choose the best model as their classification result. @cite_22 inspired from the previous work and proposed probabilistic latent semantic analysis (pLSA) incorporate with KNN for scene classification. Vogel and Schiele @cite_33 used the occurring frequency of different concepts (water, rock, etc.) in an image as the intermediate features for scene image classification. The two-stage system makes use of an intermediary semantic level of block classification (concept level) to do retrieval based on the occurrence of such concepts in an image.
@cite_34 @cite_33 @cite_22 @cite_23
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In this paper, we generate two computational models in order to estimate two dominant global properties (naturalness and openness) for representing a scene based on its global spatial structure. Naturalness and openness are two dominant perceptual properties within a multidimensional space in which semantically similar scenes (e.g., corridor and hallway) are assigned to nearby points. In this model space, the representation of a real-world scene is based on the overall shape of a scene but not on local object information. We introduce the use of a deep convolutional neural network for generating features that are well-suited for estimating the two global properties of a visual scene. The extracted features are integrated in an efficient way and fed into a linear support vector machine (SVM) to classify naturalness versus man-madeness and openness versus closedness. These two global properties (naturalness and openness) of an input image can be predicted from activations in the lowest layer of the convolutional neural network which has been trained for a scene recognition task. The consistent results of computational models in full and restricted spatial frequency ranges suggest that the representation of an image in the lowest layer of the deep scene CNN contains holistic information of the images as it leads to highest accuracy in modelling the global shape of the scene. Given a set of images of scenes containing multiple object categories (e.g. grass, roads, buildings) our objective is to discover these objects in each image in an unsupervised manner, and to use this object distribution to perform scene classification. We achieve this discovery using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature, here applied to a bag of visual words representation for each image. The scene classification on the object distribution is carried out by a k-nearest neighbour classifier. We investigate the classification performance under changes in the visual vocabulary and number of latent topics learnt, and develop a novel vocabulary using colour SIFT descriptors. Classification performance is compared to the supervised approaches of Vogel & Schiele [19] and Oliva & Torralba [11], and the semi-supervised approach of Fei Fei & Perona [3] using their own datasets and testing protocols. In all cases the combination of (unsupervised) pLSA followed by (supervised) nearest neighbour classification achieves superior results. We show applications of this method to image retrieval with relevance feedback and to scene classification in videos. We present a novel generative model for simultaneously recognizing and segmenting object and scene classes. Our model is inspired by the traditional bag of words representation of texts and images as well as a number of related generative models, including probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA). A major drawback of the pLSA and LDA models is the assumption that each patch in the image is independently generated given its corresponding latent topic. While such representation provides an efficient computational method, it lacks the power to describe the visually coherent images and scenes. Instead, we propose a spatially coherent latent topic model (spatial-LTM). Spatial-LTM represents an image containing objects in a hierarchical way by over-segmented image regions of homogeneous appearances and the salient image patches within the regions. Only one single latent topic is assigned to the image patches within each region, enforcing the spatial coherency of the model. This idea gives rise to the following merits of spatial-LTM: (1) spatial-LTM provides a unified representation for spatially coherent bag of words topic models; (2) spatial-LTM can simultaneously segment and classify objects, even in the case of occlusion and multiple instances; and (3) spatial-LTM can be trained either unsupervised or supervised, as well as when partial object labels are provided. We verify the success of our model in a number of segmentation and classification experiments. In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a "parsing graph", in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches--generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns--generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. IEEE Trans. PAMI, 24(5):657--673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.