This paper investigates a general framework to discover categories of unlabeled scene images according to their appearances (i.e., textures and structures). We jointly solve the two coupled tasks in an unsupervised manner: 1) classifying images without predetermining the number of categories and 2) pursuing generative model for each category. In our method, each image is represented by two types of image descriptors that are effective to capture image appearances from different aspects. By treating each image as a graph vertex, we build up a graph and pose the image categorization as a graph partition process. Specifically, a partitioned subgraph can be regarded as a category of scenes and we define the probabilistic model of graph partition by accumulating the generative models of all separated categories. For efficient inference with the graph, we employ a stochastic cluster sampling algorithm, which is designed based on the Metropolis–Hasting mechanism. During the iterations of inference, the model of each category is analytically updated by a generative learning algorithm. In the experiments, our approach is validated on several challenging databases, and it outperforms other popular state-of-the-art methods. The implementation details and empirical analysis are presented as well.
Most of the methods of scene image categorization involve a procedure of supervised learning, i.e., training a multi-class predictor (classifier) with the manually labeled images @cite_10 . Unsupervised image categorization is often posed as clustering images into groups according to their contents (i.e., appearances and or structures). In some traditional methods @cite_35 , various low-level features (such as color, filter banks, and textons @cite_36 ) are first extracted from images, and a clustering algorithm (e.g., @math -means or spectral clustering) is then applied to discover categories of the samples.
@cite_36 @cite_35 @cite_10
1981613567 1987496434 2028017042 88868203
In image categorization the goal is to decide if an image belongs to a certain category or not. A binary classifier can be learned from manually labeled images; while using more labeled examples improves performance, obtaining the image labels is a time consuming process. We are interested in how other sources of information can aid the learning process given a fixed amount of labeled images. In particular, we consider a scenario where keywords are associated with the training images, e.g. as found on photo sharing websites. The goal is to learn a classifier for images alone, but we will use the keywords associated with labeled and unlabeled images to improve the classifier using semi-supervised learning. We first learn a strong Multiple Kernel Learning (MKL) classifier using both the image content and keywords, and use it to score unlabeled images. We then learn classifiers on visual features only, either support vector machines (SVM) or least-squares regression (LSR), from the MKL output values on both the labeled and unlabeled images. In our experiments on 20 classes from the PASCAL VOC'07 set and 38 from the MIR Flickr set, we demonstrate the benefit of our semi-supervised approach over only using the labeled images. We also present results for a scenario where we do not use any manual labeling but directly learn classifiers from the image tags. The semi-supervised approach also improves classification accuracy in this case. Conventional supervised methods for image categorization rely on manually annotated (labeled) examples to learn good object models, which means their generality and scalability depends heavily on the amount of human effort available to help train them. We propose an unsupervised approach to construct discriminative models for categories specified simply by their names. We show that multiple-instance learning enables the recovery of robust category models from images returned by keyword-based search engines. By incorporating constraints that reflect the expected sparsity of true positive examples into a large-margin objective function, our approach remains accurate even when the available text annotations are imperfect and ambiguous. In addition, we show how to iteratively improve the learned classifier by automatically refining the representation of the ambiguously labeled examples. We demonstrate our method with benchmark datasets, and show that it performs well relative to both state-of-the-art unsupervised approaches and traditional fully supervised techniques. Most of the existing methods for natural scene categorization only consider whether a sample is relevant or irrelevant to a particular concept. However, for the samples relevant to a certain concept, their typicalities or relevancy scores to the concept generally are different. Typicality measure should be taken into account to make the categorization results more consistent with human's perception. In this paper, we propose a novel typicality ranking scheme for categorizing natural scenes through a two-stage semi-supervised multiple-instance learning method. The first stage infers the typicalities of the underlying positive instances (i.e., regions in images) in the training dataset and the second one predicts the typicality of each bag (i.e., image) in a semi-supervised manner. Compared to existing typicality ranking approaches, the main advantages of the proposed method lie in twofold. First, it only needs image-level labels instead of region-level ones in the training stage. Second, it is fully automated and no human feedback is required. Experiments conducted on a COREL image dataset demonstrate the effectiveness of the proposed approach. We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable and face semantic drift because the learning task is under-constrained. This is primarily because they ignore the strong interactions that often exist between scene categories, such as the common attributes shared across categories as well as the attributes which make one scene different from another. The goal of this paper is to exploit these relationships and constrain the semi-supervised learning problem. For example, the knowledge that an image is an auditorium can improve labeling of amphitheaters by enforcing constraint that an amphitheater image should have more circular structures than an auditorium image. We propose constraints based on mutual exclusion, binary attributes and comparative attributes and show that they help us to constrain the learning problem and avoid semantic drift. We demonstrate the effectiveness of our approach through extensive experiments, including results on a very large dataset of one million images.