With the rapid development of computer multi-media technique

database technique and computer network technique

there have been more and more images to classify and label. Instead of using traditional manual mode

it has been a hot research field to use computer-aided automatic image-scene classification techniques. Among numerous image scene classification methods

the bag-of-visual-words(BOVW)model has become a widely adopted one

which

as a middle level feature

can narrow the gap between low-level visual features and high-level semantic features. However

reviews about BOVW model in image scene classification are rarely seen on journals in China and abroad. Therefore

in order to give a comprehensive understanding of this method to researchers in this field

this paper systematically summarizes these studies. Based on numerous references about the BOVW model in image scene classification during almost the past ten years

we divide the general process of development of the BOVW into five stages

that is

the stage of direct application of early bag-of-words model in image field

the stage of studying latent semantic information in the BOVW model

the stage of studying spatial layout or structure information in the BOVW model

the stage of studying context information in the BOVW model

and the stage of optimizing visual word semantics and introducing new methods into the BOVW model. Furthermore

we sum up and compare various existing BOVW models in image scene classification in terms of local feature selection

feature generation of local image patches

visual vocabulary construction

histogram representation of bag of visual words feature

optimization of visual words

and so on. The development history of the BOVW and the research status of the BOVW based image scene classification are reviewed

which gives a clear trail of the development of the BOVW model;the numerous existing the BOVW models are categorized according to their working mechanism;the advantages and disadvantages of commonly used methods are compared;the performance evaluation method for the BOVW model is described and the commonly used standard scene databases are collected

with their best classification accuracies given separately. As a hot research field that is currently rising

studies of the BOVW methods in image scene classification have produced quite a few research progress. The research in computer vision field has no longer been limited to directly applying original the BOVW model to describe image content

and more and more differences between images and texts are considered. The urgent problems to be solved are as follows:the performance of the BOVW will be greatly influenced when the bag of visual words are applied to the samples that are quite different from the training ones

while training new bag of visual words based on new training samples is very time and labor consuming;there is still no theoretical guide for determining the size of visual vocabulary;the relationship between visual words and semantics is still not fully exploited;the application of the BOVW in special fields

such as high-resolution remote sensing land-use scene classification

is far from being satisfactory. Besides

based on these problems

there may be some interesting research directions for example: constructing universal self-adaptive bag of visual words for different sample sets

automatically selecting optimal vocabulary size

adding more spatial layout and context information to the BOVW and exploring latent semantic information in visual words

studying image visual grammars for image understanding

studying scene classification problems in images of special fields

such as high resolution remote sensing images

and investigating new well-characterized low level feature extraction algorithms to construct high level bag of visual words. To conclude

although there are still a number of urgent problems to be solved in the application of the BOVW model based image scene classification

the important meanings of the studies of the BOVW model cannot be covered up.