Keyword Visual Representation For Image Retrieval And Image Annotation

Keyword-based image retrieval is more comfortable for users than content-based image retrieval. Because of the lack of semantic description of images, image annotation is often used a priori by learning the association between the semantic concepts (keywords) and the images (or image regions). This association issue is particularly difficult but interesting because it can be used for annotating images but also for multimodal image retrieval. However, most of the association models are unidirectional, from image to keywords. In addition to that, existing models rely on a fixed image database and prior knowledge. In this paper, we propose an original association model, which provides image-keyword bidirectional transformation. Based on the state-of-the-art Bag of Words model dealing with image representation, including a strategy of interactive incremental learning, our model works well with a zero-or-weak-knowledge image database and evolving from it. Some objective quantitative and qualitative evaluations of the model are proposed, in order to highlight the relevance of the method.