A semi-automatic process, which support users in the task of annotating large image data sets, has been proposed recently. Images are clustered automatically according to similarity and are presented to the user as a sorted set. During the annotation process, partial annotations are used for further improvement of the clustering. This interactive annotation process has three important propertie...

In image retrieval, most systems lack user-centred evaluation since they are assessed by some chosen ground truth dataset. The results reported through precision and recall assessed against the ground truth are thought of as being an acceptable surrogate for the judgment of real users. Much current research focuses on automatically assigning keywords to images for enhancing retrieval effectiven...

This paper introduces an approach for automatic image annotation which is based on contextual knowledge extracted from semantic rich web documents containing images. The knowledge itself is organized in ontologies and extended by learning algorithms for new contextual information. For this purpose, the contextual background of a picture is used for the annotation process and later in the image ...

In this survey, we argue that using structured vocabularies is capital to the success of image annotation. We analyze literature on image annotation uses and user needs, and we stress the need for automatic annotation. We briefly expose the difficulties posed to machines for this task and how it relates to controlled vocabularies. We survey contributions in the field showing how structures are ...

Feature extraction and representation is a crucial step for multimedia processing. How to extract ideal features that can reflect the intrinsic content of the images as complete as possible is still a challenging problem in computer vision. However, very little research has paid attention to this problem in the last decades. So in this paper, we focus our review on the latest development in ima...

Image annotation is a method for representing an image with a suitable keyword closer to its semantic concept. Automatically assigning relevant text keywords to image is an important problem. Many algorithms and combination of different features have been proposed in the past and achieved good performance. Efforts have focused upon many other fields and some predefined set of features in the ar...

This paper presents a novel approach to Automatic Image Annotation (AIA) which combines both Hidden Markov Model (HMM) and Support Vector Machine (SVM). Typical image annotation methods directly map low-level features to high-level concepts and overlook the importance to mining the contextual information among the annotated keywords. The proposed HMM-SVM based approach comprises two different k...

In this paper we present a novel approach to personal photo album management allowing the end user to efficiently access the collection without any need for tedious manual annotation or indexing of the photos. The proposed work exploits methods and technology from the field of computer vision and pattern recognition for face detection, face representation and image annotation to automatically c...

These days automatic image annotation is an important topic and several efforts are made to solve the semantic gap problem which is still an open issue. Also, Content Based Image Retrieval (CBIR) cannot solve this problem. One of the efficient and effective models for solving the semantic gap and visual recognition and retrieval is Bag of Feature (BoF) model which can quantize local visual feat...

Image annotation is a method for representing an image with a suitable keyword closer to its semantic concept. Automatically assigning relevant text keywords to image is an important problem. Many algorithms and combination of different features have been proposed in the past and achieved good performance. Efforts have focused upon many other fields and some predefined set of features in the ar...