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very large when a dense grid is used where the histograms are computed and combined for many different points. The current dominating solution to this problem is to use a clustering method to create a visual codebook that is exploited by an appearance based descriptor to create a histogram of visual keywords present in an
Keyword-based web search engine uses text to reflect users' query intentions. However, it is hard to descript user's intention with simple text terms accurately, and besides of this, it is also hard to make the association between the text terms and images precisely. As a result, the keyword-based image search engine
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the
, the improved model is capable of discovering the correlation between blobs (segmented regions) and textual keywords so as to automatically generate keywords for un-annotated image according to joint probabilities. Moreover, it has the ability to detect and remove false keyword(s) by considering the co-occurrence of
information can aid the learning process given a fixed amount of labeled images. In particular, we consider a scenario where keywords are associated with the training images, e.g. as found on photo sharing websites. The goal is to learn a classifier for images alone, but we will use the keywords associated with labeled and
We propose a probabilistic graphical model to represent weakly annotated images. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in classification and automatic
Semantic image retrieval using text such keywords or captions at different semantic levels has attracted considerable research attention in recent years. Automatic image annotation (AIA) has been proved to be an effective and promising solution to automatically deduce the high-level semantics from low-level visual
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