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.
Subjects were asked to identify scenes after very brief exposures (<70 ms). Their performance was always above chance and improved with exposure duration, confirming that subjects can get the gist of a scene with one fixation. We propose that a simple texture analysis of the image can provide a useful cue towards rapid scene identification. Our model learns texture features across scene categories and then uses this knowledge to identify new scenes. The texture analysis leads to similar identifications and confusions as subjects with limited processing time. We conclude that early scene identification can be explained with a simple texture recognition model.  A traditional approach to retrieving images is to manually annotate the image with textual keywords and then retrieve images using these keywords. Manual annotation is expensive and recently a few approaches have been proposed for automatically annotating images. These techniques usually learn a statistical model using a training set of images annotated with keywords and use this model to automatically annotate test images. While promising, these techniques have generally been tested on a few thousand images, with vocabularies of a few hundred words or less and using relatively high quality training data where the keywords are categories objects and are directly correlated with the visual data. Here, we investigate the problem of automatically annotating a large dataset of news photographs using low quality training data and a large vocabulary. We use 56,117 images and captions from Yahoo News Photos for our training and test data. The captions in the training portion of this data often contain a great deal of text most of which does not directly describe the image and as labels are, therefore noisy. We use the Normalized Continuous Relevance Models for our annotation and discuss how to speed up the model (by a factor of 10) using a voting technique. An improved distance measure also improves precision. To handle noisy text data and the large vocabulary of 4073 words, we investigate using different kinds of words for training and show that words which describe the content of the picture are significantly more useful for annotating images. Previous work on annotating images has largely dealt with high quality keywords.