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In general visual words (VWs) exist in a feature space of continuous values implying a huge number of words and therefore a huge language. Since image retrieval systems need to use text retrieval techniques that are dependent on natural languages, which have a limit to the number of terms and words, there is a need to reduce the number of ...
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
The main idea is that a small number of candidates that are activated in parallel are subject to a serial-verification process. This model starts the word-recognition process with a basic representation of the stimulus. Then, sensory trace, consisting of line features is used to activate word detectors.
Graphics can be functional or artistic. The latter can be a recorded version, such as a photograph, or an interpretation by a scientist to highlight essential features, or an artist, in which case the distinction with imaginary graphics may become blurred. It can also be used for architecture.
A visual language is a system of communication using visual elements. Speech as a means of communication cannot strictly be separated from the whole of human communicative activity which includes the visual [1] and the term 'language' in relation to vision is an extension of its use to describe the perception, comprehension and production of visible signs.
The timing and accuracy of word recognition relies on where in the word the eye is currently fixating. Recognition is fastest and most accurate when fixating in the middle of the word. This is due to a decrease in visual acuity that results as letters are situated farther from the fixated location and become harder to see. [18]
The visual word form area (VWFA) is a functional region of the left fusiform gyrus and surrounding cortex (right-hand side being part of the fusiform face area) that is hypothesized to be involved in identifying words and letters from lower-level shape images, prior to association with phonology or semantics.