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Social and Mobile are the two very characterizing trends of the Internet. Subsequently, the volume of photos with rich social, textual and contextual information increases exponentially either on mobile devices or social networks. Performing an efficient and effective mobile Image Search over social photo collection is therefore a crucial challenge. Indeed, capture the complex connections among social...
With the great popularity of social photos sharing websites, a tremendous volume of digital images is hosted together with their associated tags. Thus, extensive research efforts have been dedicated to tag-based social image search which enables users to formulate their queries using tags. However, tag queries are often ambiguous and typically short. Diversifying search results is a common solution...
In a concept based image retrieval system, query-by-concept mapping is a new trend of query formulation using a set of predefined concepts in order to improve retrieval effectiveness. The key challenge is how to select the appropriate concepts since many of them will not directly be named in the query? In this paper, we propose a new approach for query to concept mapping based on the contextual correlations...
Recently, image retrieval approaches shift to context-based reasoning. Context-based approaches proved their efficiency to improve retrieval process. In fact, conventional image search engines are often not able to satisfy the user's intent as they provide noisy or/and redundant results. In addition, when a query is ambiguous, such systems can hardly distinguish different meanings for one query and...
Query analysis is an important phase in image retrieval process especially for ambiguous queries. This paper describes a query analysis process that manages textual and visual queries. The main idea is to select the most appropriate concepts. For the textual part, we extract keywords. Then, we deduce the most relevant concepts related to such keyword by performing a semantic similarity computing based...
Automatic photo annotation task aims to describe the semantic content by detecting high level concepts in order to further facilitate concept based video retrieval. Most of existing approaches are based on independent semantic concept detectors without considering the contextual correlation between concepts. This drawback has its impact over the efficiency of such systems. Recently, harnessing contextual...
Automatic photo annotation task aims to describe the semantic content by detecting high level concepts. Most existing approaches are performed by training independent concept detectors omitting the interdependencies between concepts. The obtained annotations are often not so satisfactory. Therefore, a process of annotation refinement is mondatory to improve the imprecise annotation results. Recently,...
The paper proposes a novel semi-automatic soft collaborative annotation scheme for video semantic indexing. To annotate video data effectively and accurately, a video collaborative soft annotation within users' judgment modeling is first proposed in this paper. We, then, introduce a semiautomatic annotation strategy which combines the active learning and self-training in order to reduce the annotators'...
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