Image Co-saliency Detection and Co-segmentation from The Perspective of Commonalities

University of Technology Sydney. Faculty of Engineering and Information Technology.Image co-saliency detection and image co-segmentation aim to identify the common salient objects and extract them in a group of images. Image co-saliency detection and image co-segmentation are important for many content-based applications such as image retrieval, image editing, and content aware image/video compression. The image co-saliency detection and image co-segmentation are very close works. The most important part in these two works is the definition of the commonality of the common objects. Usually, common objects share similar low-level features, such as appearances, including colours, textures shapes, etc. as well as the high-level semantic features. In this thesis, we explore the commonalities of the common objects in a group of images from low-level features and high-level features, and the way to achieve the commonalities and finally segment the common objects. Three main works are introduced, including an image co-saliency detection model and two image co-segmentation methods. , an image co-saliency detection model based on region-level fusion and pixel-level refinement is proposed. The commonalities between the common objects are defined by the appearance similarities on the regions from all the images. It discovers the regions that are salient in each individual image as well as salient in the whole image group. Extensive experiments on two benchmark datasets demonstrate that the proposed co-saliency model consistently outperforms the state-of-the-art co-saliency models in both subjective and objective evaluation. , an unsupervised images co-segmentation method via guidance of simple images is proposed. The commonalities are still defined by hand-crafted features on regions, colours and textures, but not calculated among regions from all the images. It takes advantages of the reliability of simple images, and successfully improves the performance. The experiments on the dataset demonstrate the outperformance and robustness of the proposed method. , a learned image co-segmentation model based on convolutional neural network with multi-scale feature fusion is proposed. The commonalities between objects are not defined by handcraft features but learned from the training data. When training a neural network with multiple input images simultaneously, the resource cost will increase rapidly with the inputs. To reduce the resource cost, reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-the-art methods while the network has successfully gotten simplified and the resources cost is reduced

An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Visual Saliency Based on Multiscale Deep Features

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this CVPR 2015 paper, we discover that a high-quality visual saliency model can be trained with multiscale features extracted using a popular deep learning architecture, convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for extracting features at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotation. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.Comment: To appear in CVPR 201