Journal ArticleDOI
Manifold Ranking-Based Matrix Factorization for Saliency Detection
Reads0
Chats0
TLDR
It is proved that the MRMF has good generalizability, and develops an efficient optimization algorithm based on the Nesterov method that enforces similar saliency values on neighboringsuperpixels and ranks superpixels according to the learned coefficients.Abstract:
Saliency detection is used to identify the most important and informative area in a scene, and it is widely used in various vision tasks, including image quality assessment, image matching, and object recognition. Manifold ranking (MR) has been used to great effect for the saliency detection, since it not only incorporates the local spatial information but also utilizes the labeling information from background queries. However, MR completely ignores the feature information extracted from each superpixel. In this paper, we propose an MR-based matrix factorization (MRMF) method to overcome this limitation. MRMF models the ranking problem in the matrix factorization framework and embeds query sample labels in the coefficients. By incorporating spatial information and embedding labels, MRMF enforces similar saliency values on neighboring superpixels and ranks superpixels according to the learned coefficients. We prove that the MRMF has good generalizability, and develops an efficient optimization algorithm based on the Nesterov method. Experiments using popular benchmark data sets illustrate the promise of MRMF compared with the other state-of-the-art saliency detection methods.read more
Citations
More filters
Journal ArticleDOI
Multimodal Deep Autoencoder for Human Pose Recovery
TL;DR: A novel pose recovery method using non-linear mapping with multi-layered deep neural network and back-propagation deep learning to obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix.
Journal ArticleDOI
Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering
TL;DR: Zhang et al. as mentioned in this paper proposed a coattention mechanism using a deep neural network (DNN) architecture to jointly learn the attentions for both the image and the question, which can reduce the irrelevant features effectively and obtain more discriminative features for image and question representations.
Journal ArticleDOI
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
TL;DR: It is demonstrated that D3Net can be used to efficiently extract salient object masks from real scenes, enabling effective background-changing application with a speed of 65 frames/s on a single GPU.
Journal ArticleDOI
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
TL;DR: A triplet-based deep hashing (TDH) network for cross-modal retrieval using the triplet labels, which describe the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross- modal instances.
Journal ArticleDOI
Feature Selection Based on Structured Sparsity: A Comprehensive Study
TL;DR: This paper compares the differences and commonalities of these methods based on regression and regularization strategies, but also provides useful guidelines to practitioners working in related fields to guide them how to do feature selection.
References
More filters
Journal ArticleDOI
A feature-integration theory of attention
Anne Treisman,Garry A. Gelade +1 more
TL;DR: A new hypothesis about the role of focused attention is proposed, which offers a new set of criteria for distinguishing separable from integral features and a new rationale for predicting which tasks will show attention limits and which will not.
Journal ArticleDOI
A model of saliency-based visual attention for rapid scene analysis
TL;DR: In this article, a visual attention system inspired by the behavior and the neuronal architecture of the early primate visual system is presented, where multiscale image features are combined into a single topographical saliency map.
A model of saliency-based visual attention for rapid scene analysis
TL;DR: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented, which breaks down the complex problem of scene understanding by rapidly selecting conspicuous locations to be analyzed in detail.
Journal ArticleDOI
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Book ChapterDOI
Shifts in selective visual attention: towards the underlying neural circuitry.
Christof Koch,Shimon Ullman +1 more
TL;DR: This study addresses the question of how simple networks of neuron-like elements can account for a variety of phenomena associated with this shift of selective visual attention and suggests a possible role for the extensive back-projection from the visual cortex to the LGN.