Effective Diversification for Ambiguous Queries in Social Image Retrieval

Ksibi, Amel; Feki, Ghada; Ammar, Anis Ben; Amar, Chokri Ben

doi:10.1007/978-3-642-40246-3_71

Cited by 14 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rate of diversity for a given image retrieval system is revealed in the obtained image organization (Feki et al, 2013). In fact, every system has its own method to declare different meanings which are generally announced through textual or visual suggestions for a next re-ranking step (Ksibi et al, 2013). However, we think that it is necessary to cover all the meanings from the first image ranking which is generally a step of an image pool text-based construction (Upadhyay et al, 2014).…”

Section: Related Work: Diversity Challenge For Image Search Enginesmentioning

confidence: 99%

Adaptive Semantic Construction for Diversity-based Image Retrieval

Feki

Ammar

Amar

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Self Cite

View full text Add to dashboard Cite

In recent years, the explosive growth of multimedia databases and digital libraries reveals crucial problems in indexing and retrieving images, what led us to develop our own approach. Our proposed approach TAD consists in disambiguating web queries to build an adaptive semantic for diversity-based image retrieval. In fact, the TAD approach is a puzzle constituted by three main components which are the TAWQU (Thesaurus-Based Ambiguous Web Query Understanding) process, the ASC (Adaptive Semantic Construction) process and the DR (Diversity-based Retrieval) process. The Wikipedia pages represent our main source of information. The NUS-WIDE dataset is the bedrock of our adaptive semantic. Actually, it permits us to perform a respectful evaluation. Fortunately, the experiments demonstrate promising results for the majority of the twelve ambiguous queries.

show abstract

Section: Related Work: Diversity Challenge For Image Search Enginesmentioning

confidence: 99%

Adaptive Semantic Construction for Diversity-based Image Retrieval

Feki

Ammar

Amar

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Self Cite

View full text Add to dashboard Cite

show abstract

“…For promoting the diversity of the top-ranked images, traditional clustering techniques have been applied as a post processing stage on the top-ranked images [18,30]. From their discriminative power, they have been shown to be effective to promote the diversity.…”

Section: Related Workmentioning

confidence: 99%

“…The users not only can quickly search for the type of image, but also can refine their search by clicking on the cluster of interest to see all the images inside the cluster. Traditional clustering algorithms have been applied to the top-ranked images [18,30]. Unfortunately, such algorithms are usually time consuming and can not be used on-the-fly.…”

Section: Introductionmentioning

confidence: 99%

Image re-ranking system based on closed frequent patterns

Voravuthikunchai

Crémilleux

Jurie

2014

Int J Multimed Info Retr

View full text Add to dashboard Cite

Text-based image retrieval is a popular and simple framework, which consists in using text annotations (e.g., image names, tags) to efficiently collect images relevant to a query word, from very large image collections. Even if the set of images retrieved using text annotations is noisy, it constitutes a reasonable initial set of images that can be considered as a bootstrap and improved further by analyzing image content. In this context, this paper introduces an approach for improving this initial set by re-ranking the so-obtained images, assuming that non-relevant images are scattered (i.e., they do not form clusters), unlike the relevant ones. More specifically, the approach consists in computing efficiently and on-the-fly closed frequent patterns, and in re-ranking images based on the number of patterns they contain. To do this, the paper introduces a simple but powerful new scoring function. Moreover, after the re-ranking process, we show how pattern mining techniques can also be applied for promoting diversity in the top-ranked images. The approach is validated on three different datasets for which state-of-the-art results are obtained.

show abstract

“…Empowered by the ubiquitous access to computer devices and the Internet, an ever-growing amount of digital images has been emerged [25]. In light of this, image retrieval is considered as an active research topic that aims at retrieving relevant images to a user query from a large database of digital images [11,14,21,26]. Until recently, most of the popular search engines (e.g., Flickr) are built upon the textual information associated with images [4,7,24].…”

Section: Introductionmentioning

confidence: 99%

Visual Re-Ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval

Bouhlel

Feki

Amar

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Visual re-ranking has received considerable attention in recent years. It aims to enhance the performance of text-based image retrieval by boosting the rank of relevant images using visual information. Hypergraph has been widely used for relevance estimation, where textual results are taken as vertices and the re-ranking problem is formulated as a transductive learning on the hypergraph. The potential of the hypergraph learning is essentially determined by the hypergraph construction scheme. To this end, in this paper, we introduce a novel data representation technique named adaptive collaborative representation for hypergraph learning. Compared to the conventional collaborative representation, we consider the data locality to adaptively select relevant and close samples for a test sample and discard irrelevant and faraway ones. Moreover, at the feature level, we impose a weight matrix on the representation errors to adaptively highlight the important features and reduce the effect of redundant/noisy ones. Finally, we also add a nonnegativity constraint on the representation coefficients to enhance the hypergraph interpretability. These attractive properties allow constructing a more informative and quality hypergraph, thereby achieving better retrieval performance than other hypergraph models. Extensive experiments on the public MediaEval benchmarks demonstrate that our re-ranking method achieves consistently superior results, compared to state-of-the-art methods.

show abstract

Effective Diversification for Ambiguous Queries in Social Image Retrieval

Cited by 14 publications

References 6 publications

Adaptive Semantic Construction for Diversity-based Image Retrieval

Adaptive Semantic Construction for Diversity-based Image Retrieval

Image re-ranking system based on closed frequent patterns

Visual Re-Ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval

Contact Info

Product

Resources

About