Salient Object Detection via Recursive Sparse Representation

Abstract

Object-level saliency detection is an attractive research field which is useful for many content-based computer vision and remote-sensing tasks. This paper introduces an efficient unsupervised approach to salient object detection from the perspective of recursive sparse representation. The reconstruction error determined by foreground and background dictionaries other than common local and global contrasts is used as the saliency indication, by which the shortcomings of the object integrity can be effectively improved. The proposed method consists of the following four steps: (1) regional feature extraction; (2) background and foreground dictionaries extraction according to the initial saliency map and image boundary constraints; (3) sparse representation and saliency measurement; and (4) recursive processing with a current saliency map updating the initial saliency map in step 2 and repeating step 3. This paper also presents the experimental results of the proposed method compared with seven state-of-the-art saliency detection methods using three benchmark datasets, as well as some satellite and unmanned aerial vehicle remote-sensing images, which confirmed that the proposed method was more effective than current methods and could achieve more favorable performance in the detection of multiple objects as well as maintaining the integrity of the object area.

Temporal and non-temporal contextual saliency analysis for generalized wide-area search within unmanned aerial vehicle (UAV) video

“…One of the first and most seminal works on visual saliency detection is [1], which has served as the basis and inspiration of many more recent methods such as [7] and [8]. These works use a bottom-up approach based on low-level features such as intensity, colour and orientation, inspired by neuroscience principles.…”

“…Due to the focus on low-level information, these approaches commonly suffered shortcomings such as reliance on priors, difficulties in detecting objects that touch the edges of the image and in detecting smaller and more subtle objects. Additionally, the approach of [8] suffered from over-detection in UAV/aerial-style images.…”

Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding

“…For instance, a new pan-sharpening method based on compressed sensing [24] is presented in [19], which employs sparse prior to regularize the degradation model and obtain competitive fusion results. Then, Li et al [20] proposed an image-fusion method based on sparse representation (SR) [25][26][27][28][29], which avoids the unavailability of HR MS images. Subsequently, SR is combined with the details injection model in [3] to further improve the quality of the fused results.…”

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

“…The detection of infrared small and small targets is to analyze the sparse components obtained by RPCA, because small and small targets are sparse compared with the infrared background. Since different setting parameters can obtain different degrees of sparse components, while the virtual alarm source is sparse compared with the infrared background, while the background is low-rank, RPCA can be used to obtain the sparse components of the infrared image, including the virtual alarm source, noise, and clutter.With the development of image algorithms, sparse representation, and dictionary learning are increasingly applied to target detection [27], image reconstruction [28], image denoising [29], image compression [30], and other aspects. Sparse representation is to express most or all of the data matrix Y with a linear combination of fewer basic signals.…”

“…With the development of image algorithms, sparse representation, and dictionary learning are increasingly applied to target detection [27], image reconstruction [28], image denoising [29], image compression [30], and other aspects. Sparse representation is to express most or all of the data matrix Y with a linear combination of fewer basic signals.…”