Visual Word

Visual words, as used in image retrieval systems, refer to small parts of an image which carry some kind of information related to the features (such as the color, shape or texture), or changes occurring in the pixels such as the filtering, low-level feature descriptors (SIFT, SURF, ...etc.). Visual words, as used in image retrieval systems, refer to small parts of an image which carry some kind of information related to the features (such as the color, shape or texture), or changes occurring in the pixels such as the filtering, low-level feature descriptors (SIFT, SURF, ...etc.). The approaches of text retrieval system (or information retrieval IR system ), which developed over 40 years, are based on keywords or Term. The advantage of these approaches is particularly due to the fact that they are effective and fast. Text-search engines are able quickly to find documents from hundreds or millions (by using vector space model ). In the same time of that, text retrieval systems have a huge success, the standard image retrieval systems (like simple search by colors, shapes...etc.) have a large number of limitations. Consequently, researchers try to take advantage from text retrieval techniques to apply them to image retrieval. That can be by a new kind of vision to understand images as textual documents, which is visual words approach. Let’s consider that the pixels of an image, which are the smallest parts in a digital images (can not be divided into smaller ones), are like the letters of an alphabetical language. Then, a set of pixels in an image (patches or arrays of pixels) is a word. Each word can then be re-processed into a morphological system to extract a term related to that word. Then, several words can share a same meaning, each one will refer to the same term (like in any language). More than one words shared the same meaning and its belong to the same term (have same information). By this view, researchers can take advantage from text retrieval techniques to apply them to image retrieval system.