Publication

Are All Pixels Equally Important? Towards Multi-Level Salient Object Detection

Gökhan Yildirim
2015
Thèse EPFL
Résumé

When we look at our environment, we primarily pay attention to visually distinctive objects. We refer to these objects as visually important or salient. Our visual system dedicates most of its processing resources to analyzing these salient objects. An analogous resource allocation can be performed in computer vision, where a salient object detector identifies objects of interest as a pre-processing step. In the literature, salient object detection is considered as a foreground-background segmentation problem. This approach assumes that there is no variation in object importance. Only the most salient object(s) are detected as foreground. In this thesis, we challenge this conventional methodology of salient-object detection and introduce multi-level object saliency. In other words, all pixels are not equally important. The well-known salient-object ground-truth datasets contain images with single objects and thus are not suited to evaluate the varying importance of objects. In contrast, many natural images have multiple objects. The saliency levels of these objects depend on two key factors. First, the duration of eye fixation is longer for visually and semantically informative image regions. Therefore, a difference in fixation duration should reflect a variation in object importance. Second, visual perception is subjective; hence the saliency of an object should be measured by averaging the perception of a group of people. In other words, objective saliency can be considered as the collective human attention. In order to better represent natural images and to measure the saliency levels of objects, we thus collect new images containing multiple objects and create a Comprehensive Object Saliency (COS) dataset. We provide ground truth multi-level salient object maps via eye-tracking and crowd-sourcing experiments. We then propose three salient-object detectors. Our first technique is based on multi-scale linear filtering and can detect salient objects of various sizes. The second method uses a bilateral-filtering approach and is capable of producing uniform object saliency values. Our third method employs image segmentation and machine learning and is robust against image noise and texture. This segmentation-based method performs the best on the existing datasets compared to our other methods and the state-of-the-art methods. The state-of-the-art salient-object detectors are not designed to assess the relative importance of objects and to provide multi-level saliency values. We thus introduce an Object-Awareness Model (OAM) that estimates the saliency levels of objects by using their position and size information. We then modify and extend our segmentation-based salient-object detector with the OAM and propose a Comprehensive Salient Object Detection (CSD) method that is capable of performing multi-level salient-object detection. We show that the CSD method significantly outperforms the state-of-the-art methods on the COS dataset. We use our salient-object detectors as a pre-processing step in three applications. First, we show that multi-level salient-object detection provides more relevant semantic image tags compared to conventional salient-object detection. Second, we employ our salient-object detector to detect salient objects in videos in real time. Third, we use multi-level object-saliency values in context-aware image compression and obtain perceptually better compression compared to standard JPEG with the same file size.

À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Concepts associés (33)
Segmentation d'image
La segmentation d'image est une opération de s consistant à détecter et rassembler les pixels suivant des critères, notamment d'intensité ou spatiaux, l'image apparaissant ainsi formée de régions uniformes. La segmentation peut par exemple montrer les objets en les distinguant du fond avec netteté. Dans les cas où les critères divisent les pixels en deux ensembles, le traitement est une binarisation. Des algorithmes sont écrits comme substitut aux connaissances de haut niveau que l'homme mobilise dans son identification des objets et structures.
Object co-segmentation
In computer vision, object co-segmentation is a special case of , which is defined as jointly segmenting semantically similar objects in multiple images or video frames. It is often challenging to extract segmentation masks of a target/object from a noisy collection of images or video frames, which involves object discovery coupled with . A noisy collection implies that the object/target is present sporadically in a set of images or the object/target disappears intermittently throughout the video of interest.
Oculométrie
vignette|Vidéo de l'étude du mouvement de l'œil avec la méthode oculométrique. L’oculométrie (en anglais pour « suivi oculaire », ou pour « suivi du regard ») regroupe un ensemble de techniques permettant d'enregistrer les mouvements oculaires. Les oculomètres les plus courants analysent des images de l'œil humain enregistrées par une caméra, souvent en lumière infrarouge, pour calculer la direction du regard du sujet. En fonction de la précision souhaitée, différentes caractéristiques de l'œil sont analysées.
Afficher plus
Publications associées (66)

SVGC-AVA: 360-Degree Video Saliency Prediction With Spherical Vector-Based Graph Convolution and Audio-Visual Attention

Pascal Frossard, Chenglin Li, Li Wei, Qin Yang, Yuelei Li, Hao Wang

Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by joint ...
Ieee-Inst Electrical Electronics Engineers Inc2024

Assessing Experiences and Visual Perception in Train Station Environments with a Mobile Application and Eye Tracking

Andrea Regula Schneider

The Customer Experience Management at the Swiss Federal Railways (SBB) experiments with new technologies and methods to capture experiences and perceptions of passengers to design safe and customer friendly environments. First, a new methodological approac ...
2022

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Gökhan Yildirim

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural im ...
2020
Afficher plus
MOOCs associés (10)
Digital Signal Processing [retired]
The course provides a comprehensive overview of digital signal processing theory, covering discrete time, Fourier analysis, filter design, sampling, interpolation and quantization; it also includes a
Digital Signal Processing
Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By rewo
Digital Signal Processing I
Basic signal processing concepts, Fourier analysis and filters. This module can be used as a starting point or a basic refresher in elementary DSP
Afficher plus

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.