Event recognition in photo albums using probabilistic graphical models and feature relevance

Bacha, Siham; Allili, Mohand Sad; Benblidia, Nadjia

doi:10.1016/j.jvcir.2016.07.021

Cited by 11 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy (%) AgS [3] 41.43 ShMM [3] 55.71 Method in [17] 73.41 R-OS-PGM [18] 74.28 Our method (best iteration) 85.0…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Event Recognition in Personal Photo Collections: An Active Learning Approach

Ahmad¹,

Mekhalfi²,

Conci³

2018

View full text Add to dashboard Cite

In this paper, we propose an active learning based approach to event recognition in personal photo collections to tackle the challenges due to the weakly labeled data, and the presence of irrelevant pictures in personal photo collections. Conventional approaches relying on supervised learning can not identify the relevant samples in training albums, often leading to wrong classification. In our work, we aim to utilize the concepts of active learning to choose the most relevant samples from a collection and train a classifier. We also investigate the importance of relevant images in the event recognition process, and show how the performance degrades if all images from an album, containing the irrelevant ones, are included in the process. The experimental evaluation is carried out on a benchmark dataset composed of a large number of personal photo albums. We demonstrate that the proposed strategy yields encouraging scores in the presence of irrelevant images in personal photo collections, advancing recent leading works.

show abstract

“…Accuracy (%) AgS [3] 41.43 ShMM [3] 55.71 Method in [17] 73.41 R-OS-PGM [18] 74.28 Our method (best iteration) 85.0…”

Section: Methodsmentioning

confidence: 99%

“…More recently, in [17], a deep model is trained on images from personal photo collections to capture the co-occurrences, and frequencies of images features. In [18], features extracted through a deep model are used in a graphical model to capture a link among the pictures in a photo album.…”

Section: Related Workmentioning

confidence: 99%

Event Recognition in Personal Photo Collections: An Active Learning Approach

Ahmad¹,

Mekhalfi²,

Conci³

2018

View full text Add to dashboard Cite

show abstract

“…An original approach was taken by [6] who casted photo collections as sequential data and treated sub-events as latent variables associated to each image in an Hidden Markov Models and learned them while training the event classifier. More recently, [8] proposed a probabilistic graphical model to predict the event categories of groups of photos, that relies on high-level visual features such as objects and scenes extracted directly from images by employing a deep learning based approach.…”

Section: Event Recognition In Photo Albumsmentioning

confidence: 99%

Smartphone picture organization: A hierarchical approach

Lönn

Radeva

Dimiccoli

2019

Computer Vision and Image Understanding

View full text Add to dashboard Cite

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.

show abstract

“…Developing robust and reliable models for the automatic recognition of scenes is of importance in the field of intelligent systems and artificial intelligence since it directly supports real-life applications. For instance, Scene and event recognition has been previously addressed in the literature [1,29]. Scene recognition for robot localization with indoor localization for mobile robots is one of the emerging application scopes of scene recognition [2,5,21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Scene Recognition from Visual Data: A Survey

Matei

Glavan

Talavera

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The use of deep learning techniques has exploded during the last few years, resulting in a direct contribution to the field of artificial intelligence. This work aims to be a review of the state-of-the-art in scene recognition with deep learning models from visual data. Scene recognition is still an emerging field in computer vision, which has been addressed from a single image and dynamic image perspective. We first give an overview of available datasets for image and video scene recognition. Later, we describe ensemble techniques introduced by research papers in the field. Finally, we give some remarks on our findings and discuss what we consider challenges in the field and future lines of research. This paper aims to be a future guide for model selection for the task of scene recognition.

show abstract

Event recognition in photo albums using probabilistic graphical models and feature relevance

Cited by 11 publications

References 23 publications

Event Recognition in Personal Photo Collections: An Active Learning Approach

Event Recognition in Personal Photo Collections: An Active Learning Approach

Smartphone picture organization: A hierarchical approach

Deep Learning for Scene Recognition from Visual Data: A Survey

Contact Info

Product

Resources

About