@article{laib_probabilistic_2019,

title = {A probabilistic topic model for event-based image classification and multi-label annotation},

author = {L. Laib and M. S. Allili and S. Ait-Aoudia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067936924&doi=10.1016%2fj.image.2019.05.012&partnerID=40&md5=a617885b93f3a931c6b6ce1a165f940b},

doi = {10.1016/j.image.2019.05.012},

issn = {09235965 (ISSN)},

year  = {2019},

date = {2019-01-01},

journal = {Signal Processing: Image Communication},

volume = {76},

pages = {283–294},

abstract = {We propose an enhanced latent topic model based on latent Dirichlet allocation and convolutional neural nets for event classification and annotation in images. Our model builds on the semantic structure relating events, objects and scenes in images. Based on initial labels extracted from convolution neural networks (CNNs), and possibly user-defined tags, we estimate the event category and final annotation of an image through a refinement process based on the expectation–maximization (EM)algorithm. The EM steps allow to progressively ascertain the class category and refine the final annotation of the image. Our model can be thought of as a two-level annotation system, where the first level derives the image event from CNN labels and image tags and the second level derives the final annotation consisting of event-related objects/scenes. Experimental results show that the proposed model yields better classification and annotation performance in the two standard datasets: UIUC-Sports and WIDER. © 2019 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {Annotation performance, Classification (of information), Convolution, Convolution neural network, Convolutional neural nets, Event classification, Event recognition, Image annotation, Image Enhancement, Latent Dirichlet allocation, Multi-label annotation, Neural networks, Probabilistic topic models, Semantics, Statistics, Topic Modeling},

pubstate = {published},

tppubtype = {article}

}

@article{bacha_event_2016,

title = {Event recognition in photo albums using probabilistic graphical models and feature relevance},

author = {S. Bacha and M. S. Allili and N. Benblidia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992111208&doi=10.1016%2fj.jvcir.2016.07.021&partnerID=40&md5=20ebb156819c8fcae6e28949046ceb6e},

doi = {10.1016/j.jvcir.2016.07.021},

issn = {10473203 (ISSN)},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings - International Conference on Pattern Recognition},

journal = {Journal of Visual Communication and Image Representation},

volume = {40},

number = {Part B},

pages = {546–558},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This paper proposes a method for event recognition in photo albums which aims at predicting the event categories of groups of photos. We propose a probabilistic graphical model (PGM) for event prediction based on high-level visual features consisting of objects and scenes, which are extracted directly from images. For better discrimination between different event categories, we develop a scheme to integrate feature relevance in our model which yields a more powerful inference when album images exhibit a large number of objects and scenes. It allows also to mitigate the influence of non-informative images usually contained in the albums. The performance of the proposed method is validated using extensive experiments on the recently-proposed PEC dataset containing over 61 000 images. Our method obtained the highest accuracy which outperforms previous work. © 2016 Elsevier Inc.},

note = {Publisher: Academic Press Inc.},

keywords = {Event prediction, Event recognition, Feature relevance, Graphic methods, Object/scene relevance, Photo album, Photo albums, Probabilistic graphical models, Probabilistic graphical models (PGM), Speech recognition, Visual feature},

pubstate = {published},

tppubtype = {article}

}