Skip to Main content Skip to Navigation

Assessment of photos in albums based on aesthetics and context

Abstract : An automatic photo assessment can significantly aid the process of photo selection within photo collections. However, existing computational methods approach this problem in an independent manner, by evaluating each image apart from other images in a photo album. In this thesis, we explore the modeling of photo context via a clustering approach for photo collections and the possibility of applying such context information in photo assessment. To better understand user actions within photo albums, we conduct experimental user studies, where we study how users cluster and select photos in photo collections. We estimate the level of agreement between users and investigate how the context, defined by similar photos in corresponding clusters, influences their decisions. After studying the nature of user decisions, we propose a computational approach to model user behavior. First, we introduce a hierarchical clustering method, which allows to group similar photos according to a multi-level similarity structure, based on visual descriptors. Then, the photo context information is extracted from the obtained cluster data and used to adapt a pre-computed independent photo score, using the statistics-based data and a machine learning approach. In addition, as the majority of recent methods for photo assessment are based on convolutional neural networks, we explore and visualize the aesthetic characteristics learned by such methods.
Document type :
Complete list of metadata

Cited literature [194 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Friday, November 22, 2019 - 9:26:30 AM
Last modification on : Saturday, May 22, 2021 - 3:40:09 AM


Version validated by the jury (STAR)


  • HAL Id : tel-02345620, version 2


Dmitry Kuzovkin. Assessment of photos in albums based on aesthetics and context. Image Processing [eess.IV]. Université Rennes 1, 2019. English. ⟨NNT : 2019REN1S032⟩. ⟨tel-02345620v2⟩



Record views


Files downloads