Knowledge structures: Which one to use for the query disambiguation?

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

doi:10.1109/isda.2015.7489166

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Information retrieval models define a representation of documents and the queries as well as a correspondence function which makes it possible to calculate similarities between documents and queries and to rank the results. Whatever the research model and the calculations are purely numerical and rely essentially on the frequency of words and analysis of their distribution, the search for semantic information seeks to go beyond this approach by injecting knowledge [Fek15]. For that purpose, we choose to adapt Relational Network (RN) by using Neural Tensor Network instead of Multi-layer Perceptron.…”

Section: Image Retrievalmentioning

confidence: 99%

A new model driven architecture for deep learning-based multimodal lifelog retrieval

Abdallah¹,

Feki²,

Ammar³

et al. 2018

CSRN

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Nowadays, taking photos and recording our life are daily task for the majority of people. The recorded information helped to build several applications like the self-monitoring of activities, memory assistance and long-term assisted living. This trend, called lifelogging, interests a lot of research communities such as computer vision, machine learning, human-computer interaction, pervasive computing and multimedia. Great effort have been made in the acquisition and the storage of captured data but there are still challenges in managing, analyzing, indexing, retrieving, summarizing and visualizing these captured data. In this work, we present a new model driven architecture for deep learning-based multimodal lifelog retrieval, summarization and visualization. Our proposed approach is based on different models integrated in an architecture established on four phases. Based on Convolutional Neural Network, the first phase consists of data preprocessing for discarding noisy images. In a second step, we extract several features to enhance the data description. Then, we generate a semantic segmentation to limit the search area in order to better control the runtime and the complexity. The second phase consist in analyzing the query. The third phase which based on Relational Network aims at retrieving the data matching the query. The final phase treat the diversity-based summarization with k-means which offers, to lifelogger, a key-frame concept and context selection-based visualization.

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Section: Image Retrievalmentioning

confidence: 99%

A new model driven architecture for deep learning-based multimodal lifelog retrieval

Abdallah¹,

Feki²,

Ammar³

et al. 2018

CSRN

Self Cite

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“…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]. Nevertheless, they cannot comprehensively describe the rich content of images since they totally ignore the visual information [10].…”

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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“…Thereby, the user has more ambiguity in finding the most relevant content for his information need [44], [46]. The recommendation systems serve as information filtering tools which are useful for helping users in discovering new contents, services and products they probably are interested in.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Recommendation: Current Issues and Challenges

Fakhfakh¹,

Ammar²,

Amar³

2017

ijacsa

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Abstract-Due to the revolutionary advances of deep learning achieved in the field of computer vision, object recognition and natural language processing, the deep learning gained much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness. The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances.

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Knowledge structures: Which one to use for the query disambiguation?

Cited by 6 publications

References 18 publications

A new model driven architecture for deep learning-based multimodal lifelog retrieval

A new model driven architecture for deep learning-based multimodal lifelog retrieval

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

Deep Learning-Based Recommendation: Current Issues and Challenges

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