Images are analyzed with edge and color values. Pixel information is used in the color property extraction. Texture and contrast are pixel based features. Shape or edge features are used to represent images. The images are assigned with their category values. The image features are used in the classification process. Classification techniques are used to assign labels to the images. Color const...

This paper presents a novel approach based on contextual Bayesian networks (CBN) for natural scene modeling and classification. The structure of the CBN is derived based on domain knowledge, and parameters are learned from training images. For test images, the hybrid streams of semantic features of image content and spatial information are piped into the CBN-based inference engine, which is cap...

In this paper, images of realworld natural scenes and manmade structures of different depth are taken. With increase in image depth , roughness increases in case of man-made structures whereas natural scene images become smooth, thus reducing the local roughness of the picture. Such kind of specific arrangement produces a particular spatial pattern of dominant orientations and scales that can b...

Explorative eye movements specifically target some parts of a scene while ignoring others. Here, we investigate how local image structure--defined by spatial frequency contrast--and informative image content--defined by higher order image statistics-are weighted for the selection of fixation points. We measured eye movements of macaque monkeys freely viewing a set of natural and manipulated ima...

Neuroimaging studies have found distinct patterns of response to different categories of scenes. However, the relative importance of low-level image properties in generating these response patterns is not fully understood. To address this issue, we directly manipulated the low level properties of scenes in a way that preserved the ability to perceive the category. We then measured the effect of...

The appearance of an outdoor scene depends on a variety of factors such as viewing geometry, scene structure and reflectance (BRDF or BTF), illumination (sun, moon, stars, street lamps), atmospheric condition (clear air, fog, rain) and weathering (or aging) of materials. Over time, these factors change, altering the way a scene appears. A large set of images is required to study the entire vari...

What information do people use to categorize scenes? Computational scene classification models have proposed that unlocalized amplitude information, the distribution of spatial frequencies and orientations, is useful for categorizing scenes. Previous research has provided conflicting results regarding this claim. Our previous research (Loschky et al., 2007) has shown that randomly localizing am...

Attention Restoration Theory (ART) states that built scenes place greater load on attentional resources than natural scenes. This is explained in terms of "hard" and "soft" fascination of built and natural scenes. Given a lack of direct empirical evidence for this assumption we propose that perceptual saliency of scene content can function as an empirically derived indicator of fascination. Sal...

In this paper, we deal with extraction of textual information from scene images. So far, the task of Scene Text Recognition (STR) has only been focusing on recognition of isolated words and, for simplicity, it omits words which are too short. Such an approach is not suitable for further processing of the extracted text. We define a new task which aims at extracting coherent blocks of text from ...