Visual Saliency Estimation via Attribute Based Classifiers and Conditional Random Field

2016-05-19
Demirel, Berkan
Cinbiş, Ramazan Gökberk
İKİZLER CİNBİŞ, NAZLI
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
38
views
0
downloads
Visual Saliency Estimation is a computer vision problem that aims to find the regions of interest that are frequently in eye focus in a scene or an image. Since most computer vision problems require discarding irrelevant regions in a scene, visual saliency estimation can be used as a preprocessing step in such problems. In this work, we propose a method to solve top-down saliency estimation problem using Attribute Based Classifiers and Conditional Random Fields (CRF). Experimental results show that attribute-based classifiers encode visual information better than low level features and the presented approach generates promising results compared to state-of-theart approaches on Graz-02 dataset.
Top-down saliency estimation, Discriminative dictionary, Conditional random field, Attribute
https://hdl.handle.net/11511/37474
Department of Computer Engineering, Conference / Seminar

Suggestions

Efficient detection and tracking of salient regions for visual processing on mobile platforms
Serhat, Gülhan; Saranlı, Afşar; Department of Electrical and Electronics Engineering (2009)
Visual Attention is an interesting concept that constantly widens its application areas in the field of image processing and computer vision. The main idea of visual attention is to find the locations on the image that are visually attractive. In this thesis, the visually attractive regions are extracted and tracked in video sequences coming from the vision systems of mobile platforms. First, the salient regions are extracted in each frame and a feature vector is constructed for each one. Then Scale Invaria...
Visual object detection and tracking using local convolutional context features and recurrent neural networks
Kaya, Emre Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018)
Visual object detection and tracking are two major problems in computer vision which have important real-life application areas. During the last decade, Convolutional Neural Networks (CNNs) have received significant attention and outperformed methods that rely on handcrafted representations in both detection and tracking. On the other hand, Recurrent Neural Networks (RNNs) are commonly preferred for modeling sequential data such as video sequences. A novel convolutional context feature extension is introduc...
VISUAL FIELD TESTING USING FORECASTING AND VIRTUAL REALITY
Bülbül, Emre; Akar, Gözde; Department of Electrical and Electronics Engineering (2022-2)
The standard method for assessing a patient’s visual field is visual field testing. Many diseases, including glaucoma, which affects more than 80 million people, require visual field testing for monitoring and diagnosis. The testing is done by sending light to fixed locations with different luminosities while the patient is fixating at a certain point, then the sensitivities to light at each location are determined by recording the responses of the patient to seen stimuli. Because of their form and digital ...
Object detection through search with a foveated visual system
Akbaş, Emre (2017-10-01)
Humans and many other species sense visual information with varying spatial resolution across the visual field (foveated vision) and deploy eye movements to actively sample regions of interests in scenes. The advantage of such varying resolution architecture is a reduced computational, hence metabolic cost. But what are the performance costs of such processing strategy relative to a scheme that processes the visual field at high spatial resolution? Here we first focus on visual search and combine object det...
Human motion analysis via axis based representations
Erdem, Sezen; Tarı, Zehra Sibel; Department of Computer Engineering (2007)
Visual analysis of human motion is one of the active research areas in computer vision. The trend shifts from computing motion fields to understanding actions. In this thesis, an action coding scheme based on trajectories of the features calculated with respect to a part based coordinate system is presented. The part based coordinate system is formed using an axis based representation. The features are extracted from images segmented in the form of silhouettes. We present some preliminary experiments that d...
Citation Formats
B. Demirel, R. G. Cinbiş, and N. İKİZLER CİNBİŞ, “Visual Saliency Estimation via Attribute Based Classifiers and Conditional Random Field,” presented at the 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37474.
METU IIS - Integrated Information Service open@metu.edu.tr