Adaptive Semantic Construction for Diversity-based Image Retrieval

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

doi:10.5220/0005157104440449

Cited by 8 publications

(4 citation statements)

References 5 publications

(5 reference statements)

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“…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

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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.

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Section: Introductionmentioning

confidence: 99%

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

Bouhlel

Feki

Amar

2020

Lecture Notes in Computer Science

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“…In recent years, there has been extensive research on implementing thesauri in different types of digital information systems (Alonso Gaona García et al , 2014; Dalmau et al , 2005; Feki et al , 2014; Hienert et al , 2011; Lüke et al , 2012; Shiri et al , 2013). A search of the literature revealed that the majority of researchers deployed a thesaurus in real digital information systems (Alonso Gaona García et al , 2014; Dalmau et al , 2005; Feki et al , 2014; Hienert et al , 2011; Lüke et al , 2012; Shiri et al , 2013); some reported its implementation in a pilot study or in a prototype/model digital information system (Alani et al , 2000; Blocks et al , 2006; Shiri et al , 2004), and a few proposed models for its deployment in digital information systems (Maso-Maresma and Sebastia-Salat, 2013; Nakashima et al , 2003; Petrič et al , 2011). Thesauri have been used in digital information systems mainly for three purposes: indexing, searching and browsing.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, many researchers report its use in all three purposes (Atherton, 2002; Binding and Tudhope, 2004; Blocks et al , 2006; Hienert et al , 2011; Petrič et al , 2011). Furthermore, a couple of researchers used it in both indexing and searching (Shiri et al , 2011; Soo et al , 2003; Torres and Reis, 2008) and a few reported its use only in searching (Bakar and Rahman, 2003; Feki et al , 2014; Sarmento et al , 2008; Thangaraj and Gayathri, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effectiveness of thesauri in digital information retrieval systems

Sunny

Angadi

2018

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Purpose The purpose of this study is to carry out a systematic literature review for evidence-based assessment of the effectiveness of thesaurus in digital information retrieval systems. It also aimed to identify the evaluation methods, evaluation measures and data collection tools which may be used in evaluating digital information retrieval systems. Design/methodology/approach A systematic literature review (SLR) of 344 publications from LISA and 238 from Scopus has been carried out to identify the evaluation studies for analysis, and 15 evaluation studies have been analyzed. Findings This study presents evidences for the effectiveness of thesaurus in digital information retrieval systems. Various methods for evaluating digital information systems have been identified. Also, a wide range of evaluation measures and data collection tools have been identified. Research limitations/implications The study was limited to the literature published in English language and indexed in LISA and Scopus. The evaluation methods, evaluation measures and data collection tools identified in this study may be used to design more cognizant evaluation studies for digital information retrieval systems. Practical implications The findings have significant implications for the administrators of any type of digital information retrieval systems in making more informed decisions toward implementation of thesaurus in resource description and access to digital collections. Originality/value This study extends our knowledge on the potentials of thesauri in digital information retrieval systems. It also provides cues for designing more cognizant evaluation studies for digital information systems.

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“…However, their performance capabilities are limited because of the semantic gap between the low level descriptors and the user understanding [7] [8] [9] . Besides, various types of social features [10][II] [12] [13](tags, locations, users, ... ) tend to effectively describe the semantic of images with the help of content features.…”

Section: Introductionmentioning

confidence: 99%

Semantic-aware framework for Mobile Image Search

Bouhlel

Ksibi

Ammar

et al. 2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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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 photos is as important as speeding up similarity search at large scale. This paper present a generic Mobile Image Search framework with hypergraph hashing. On the mobile side, users are enabled to formulate whether visual, textual or vocal queries. On the server side, we start by modeling complex connections that may exist among photos and social features using an hypergraph. To accelerate the nearest neighbor search over the hypergraph, a spectral hashing is performed. Namely, each hypergraph vertex is mapped to a binary string without loss of similarity. For unseen items in the hypergraph, a query-adaptive supervised learning is carried out to learn binary strings based on the query type. We report the initial results over NUS-WIDE collection which show that the proposed framework is promising in the field of Mobile Image Search.

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Adaptive Semantic Construction for Diversity-based Image Retrieval

Cited by 8 publications

References 5 publications

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

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

Evaluating the effectiveness of thesauri in digital information retrieval systems

Semantic-aware framework for Mobile Image Search

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