In this paper, we focus on the development of whole-scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-exist...

The semantic interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. We intend to design classifiers able to annotate images with keywords. Firstly, we propose an image representation appropriate for scene description: images are segmented into regions and indexed according to the presence of given region types. Secondly, w...

Text information in natural scene images serves as important clues for many computer vision applications such as content-based image retrieval, tourist translator, and assistive navigation. Extraction of such information from natural scene images, involves number of sub stages represented by text information extraction (TIE) system. However, performance of such system is greatly influenced by t...

In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce s...

Visual neural representation is constrained by the statistical properties of the environment. Prior analysis of cone pigment excitations for natural images revealed three principal components corresponding to the major retinogeniculate pathways identified by anatomical and physiological studies in primates. Here, principal component analyses were conducted on the excitations of rhodopsin, cone ...

Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces. However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal. Here we develop a new approximate Bayesian method for decoding natural images from the spiking activity of p...

Estimating local surface orientation (slant and tilt) is fundamental to recovering the three-dimensional structure of the environment. It is unknown how well humans perform this task in natural scenes. Here, with a database of natural stereo-images having groundtruth surface orientation at each pixel, we find dramatic differences in human tilt estimation with natural and artificial stimuli. Est...

Why do some images stick in our minds while others fade away? Recent work suggests that this is partially due to intrinsic differences in image content (Isola 2011, Bainbridge 2013, Borkin 2013). However, the context in which an image appears can also affect its memorability. Previous studies have found that images that are distinct, interfere less with other images in memory and are thus bette...

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This project proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are...