Kernel regression in mixed feature spaces for spatio-temporal saliency detection

Li, Yansheng; Tan, Yihua; Yu, Jin-Gang; Qi, Shengxiang; Tian, Jinwen

doi:10.1016/j.cviu.2015.01.011

Cited by 25 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since combining saliency maps from different features is a very important component in visual attention, various approaches have been proposed in previous work [4][5][6]11]. The most commonly used ones include multiplicative fusion [11], maximum fusion [28], and additive fusion [1,4,14,21]. Thereinto, multiplicative fusion emphasizes that only when all responses at a certain location are high in each saliency map, could it be regarded as a salient point.…”

Section: Mutual Consistency-guided Spatial Saliency Generationmentioning

confidence: 99%

“…In this section, we evaluate the effect of our mutual consistency-guided spatial saliency map generation scheme, and also compare it with four state-of-the art fusion methods, i.e., multiplicative fusion [11], maximum fusion [28], additive fusion using fixed weights [33], and additive fusion based on variance [14]. For the fixed weights-based additive fusion method, the weights for LC-SM and C-SM are set as w L ¼ w C ¼ 0:5.…”

Section: Evaluation Of Mutual Consistency-guided Spatial Saliency Genmentioning

confidence: 99%

See 1 more Smart Citation

Saliency detection using mutual consistency-guided spatial cues combination

Wang

Chen

2015

Infrared Physics & Technology

View full text Add to dashboard Cite

Section: Mutual Consistency-guided Spatial Saliency Generationmentioning

confidence: 99%

Section: Evaluation Of Mutual Consistency-guided Spatial Saliency Genmentioning

confidence: 99%

Saliency detection using mutual consistency-guided spatial cues combination

Wang

Chen

2015

Infrared Physics & Technology

View full text Add to dashboard Cite

“…However it is not easy to get this job done because of the loss of much original information. In the computer vision field, salient object detection is becoming one of the mainstream generic object detection methods [ 24 , 25 ]. The key idea is to build the saliency map using local contrast.…”

Section: Gradient Textural Saliency Mapmentioning

confidence: 99%

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map

Tan

et al. 2015

Sensors

Self Cite

View full text Add to dashboard Cite

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.

show abstract

“…Although greedy metric fusion can utilize multiple features to calculate the similarity between images, its use would be not ideal when the super feature vectors in Equation (2) are highly hybrid [26]. Accordingly, how to fully incorporate the merit of multiple features for measuring the affinity between two images deserves more explanation.…”

Section: Greedy Affinity Metric Fusionmentioning

confidence: 99%

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Zhang

Tao

et al. 2016

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.Keywords: high-resolution remote sensing image management; content-based high-resolution remote sensing image retrieval (CB-HRRS-IR); unsupervised feature learning; collaborative affinity metric fusion

show abstract

Kernel regression in mixed feature spaces for spatio-temporal saliency detection

Cited by 25 publications

References 51 publications

Saliency detection using mutual consistency-guided spatial cues combination

Saliency detection using mutual consistency-guided spatial cues combination

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Contact Info

Product

Resources

About