Gangshan Wu
Nanjing University
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Publication
Featured researches published by Gangshan Wu.
international conference on image processing | 2014
Ran Ju; Ling Ge; Wenjing Geng; Tongwei Ren; Gangshan Wu
Most previous works on saliency detection are dedicated to 2D images. Recently it has been shown that 3D visual information supplies a powerful cue for saliency analysis. In this paper, we propose a novel saliency method that works on depth images based on anisotropic center-surround difference. Instead of depending on absolute depth, we measure the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration. Besides, two common priors based on depth and location are used for refinement. The proposed method works within a complexity of O(N) and the evaluation on a dataset of over 1000 stereo images shows that our method outperforms state-of-the-art.
international conference on multimedia and expo | 2009
Tongwei Ren; Yan Liu; Gangshan Wu
This paper proposes a novel image retargeting technique based on global energy optimization. Most existing methods enhance the high energy parts of the original image by pre-defined strategies or local optimization based iterations. They can not achieve the global optimal effect in energy retainment. To solve this problem, our approach formulates image retargeting as a global optimization problem on energy. We first calculate the energy map of the original image. Then, we utilize a constrained linear programming to maximize the retained energy in retargeting. Finally, we propose a pixel fusion based method to generate the retargeted image. To make it more feasible in implementation, we further provide two strategies to reduce the time cost of our approach. We demonstrate the proposed approach by comparing with typical image retargeting methods.
Signal Processing-image Communication | 2015
Ran Ju; Yang Liu; Tongwei Ren; Ling Ge; Gangshan Wu
Most previous works on salient object detection concentrate on 2D images. In this paper, we propose to explore the power of depth cue for predicting salient regions. Our basic assumption is that a salient object tends to stand out from its surroundings in 3D space. To measure the object-to-surrounding contrast, we propose a novel depth feature which works on a single depth map. Besides, we integrate the 3D spatial prior into our method for saliency refinement. By sparse sampling and representing the image using superpixels, our method works very fast, whose complexity is linear to the image resolution. To segment the salient object, we also develop a saliency based method using adaptive thresholding and GrabCut. The proposed method is evaluated on two large datasets designed for depth-aware salient object detection. The results compared with several state-of-the-art 2D and depth-aware methods show that our method has the most satisfactory overall performance. HighlightsWe proposed a new depth feature for salient region detection.Spatial prior is integrated for saliency refinement.A saliency-based object segmentation method is presented.We built the largest dataset for depth-aware salient object detection evaluation.
acm multimedia | 2015
Rajiv Ratn Shah; Anwar Dilawar Shaikh; Yi Yu; Wenjing Geng; Roger Zimmermann; Gangshan Wu
Due to the ubiquitous availability of smartphones and digital cameras, the number of photos/videos online has increased rapidly. Therefore, it is challenging to efficiently browse multimedia content and obtain a summary of an event from a large collection of photos/videos aggregated in social media sharing platforms such as Flickr and Instagram. To this end, this paper presents the EventBuilder system that enables people to automatically generate a summary for a given event in real-time by visualizing different social media such as Wikipedia and Flickr. EventBuilder has two novel characteristics: (i) leveraging Wikipedia as event background knowledge to obtain more contextual information about an input event, and (ii) visualizing an interesting event in real-time with a diverse set of social media activities. According to our initial experiments on the YFCC100M dataset from Flickr, the proposed algorithm efficiently summarizes knowledge structures based on the metadata of photos/videos and Wikipedia articles.
international conference on multimedia and expo | 2014
Xiangyang Xu; Wenjing Geng; Ran Ju; Yang Yang; Tongwei Ren; Gangshan Wu
Recent years, the stereo image has become an emerging media in the field of 3D technology, which leads to an urgent demand of stereo image retrieval. In this paper, we attempt to introduce a framework for object-based stereo image retrieval (OBSIR), which retrieves images containing the similar objects to the one captured in the query image by the user. The proposed approach consists of both online and offline procedures. In the offline procedure, we propose a salient object segmentation method making use of both color and depth to extract objects from each image. The extracted objects are then represented by multiple visual feature descriptors. In order to improve the image search efficiently, we construct an approximate nearest neighbor (ANN) index using cluster-based locality sensitive hashing (LSH). In the online stage, the user may supply the query object by selecting a region of interest (ROI) in the query image, or clicking one of the objects recommended by the salient object detector. For the image retrieval evaluation we build a new dataset containing over 10K stereo images. The experiments on this dataset show that the proposed method can effectively recommend the correct object and the final retrieval result is also better than other baseline methods.
acm multimedia | 2009
Tongwei Ren; Yan Liu; Gangshan Wu
Image retargeting aims to adapt images to various screens with small sizes and arbitrary aspect ratios. In this paper, we propose a novel image retargeting approach based on region warping, which emphasizes the image parts with important content while reducing the visual distortion over the whole image. First, the original image is decomposed into homogeneous regions and further represented by curve-edge trapezoid meshes. Then, two kinds of energy maps, importance map and sensitivity map, are calculated by visual attention model and weighted gradient map respectively. With mesh representation and energy map constraints, image retargeting is formulated to a constrained optimization problem of mesh vertexes relocation. Finally, the target image is generated by separately warping the regions based on the deduced optimal solution. The experiments on different images demonstrate the effective and efficiency of our algorithm.
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics | 2010
Xuehou Tan; Gangshan Wu
Monitoring and surveillance are important aspects in modern wireless sensor networks. In applications of wireless sensor networks, it often asks for the sensors to quickly move from the interior of a specified region to the regions perimeter, so as to form a barrier coverage of the region. The region is usually given as a simple polygon or even a circle. In comparison with the traditional concept of full area coverage, barrier coverage requires fewer sensors for detecting intruders, and can thus be considered as a good approximation of full area coverage. In this paper, we present an O(n2.5 log n) time algorithm for moving n sensors to the perimeter of the given circle such that the new positions of sensors form a regular n-gon and the maximum of the distances travelled by mobile sensors is minimized. This greatly improves upon the previous time bound O(n3.5 log n). Also, we describe an O(n4) time algorithm for moving n sensors, whose initial positions are on the perimeter of the circle, to form a regular n-gon such that the sum of the travelled distances is minimized. This solves an open problem posed in [2]. Moreover, our algorithms are simpler and have more explicit geometric flavor.
Multimedia Tools and Applications | 2016
Tongwei Ren; Yan Liu; Ran Ju; Gangshan Wu
Location information, i.e., the position of content in image plane, is considered as an important supplement in saliency detection. The effect of location information is usually evaluated by integrating it with the selected saliency detection methods and measuring the improvement, which is highly influenced by the selection of saliency methods. In this paper, we provide direct and quantitative analysis of the importance of location information for saliency detection in natural images. We firstly analyze the relationship between content location and saliency distribution on four public image datasets, and validate the distribution by simply treating location based Gaussian distribution as saliency map. To further validate the effectiveness of location information, we propose a location based saliency detection approach, which completely initializes saliency maps with location information and propagate saliency among patches based on color similarity, and discuss the robustness of location information’s effect. The experimental results show that location information plays a positive role in saliency detection, and the proposed method can outperform most state-of-the-art saliency detection methods and handle natural images with different object positions and multiple salient objects.
international conference on multimedia and expo | 2008
Tongwei Ren; Yanwen Guo; Gangshan Wu; Fuyan Zhang
In this paper, we present a new approach for retargeting large images to mobile devices with small screens. As the core of image retargeting, information fidelity is adequately considered in terms of reservations of salient regions, edge integrity, and image layout. By taking these aspects as constraints, image retargeting is formulated as a constrained sampling task. Each pixel in image is first represented with a vector encoding the constraints. Then, pixels with the same vector values combine to form blocks, and the original image is thus converted into a graph representation. Thereafter, the sampling ratio of each block is determined with a balanced minimum cost flow algorithm. Final result is generated by an interpolated sampling scheme and direct scaling. Experiments demonstrate the effectiveness of the proposed approach.
pacific rim conference on multimedia | 2015
Ling Ge; Ran Ju; Tongwei Ren; Gangshan Wu
In this paper, we propose a novel interactive image segmentation method for RGB-D images using hierarchical Graph Cut. Considering the characteristics of RGB channels and depth channel in RGB-D image, we utilize Euclidean distance on RGB space and geodesic distance on 3D space to measure how likely a pixel belongs to foreground or background in color and depth respectively, and integrate the color cue and depth cue into a unified Graph Cut framework to obtain the optimal segmentation result. Moreover, to overcome the low efficiency problem of Graph Cut in handling high resolution images, we accelerate the proposed method with hierarchical strategy. The experimental results show that our method outperforms the state-of-the-art methods with high efficiency.