Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a fully supervised setting involving pixel-level annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image. First, the probabilistic contribution of each image region to the confidence of a CNN-based image classifier is computed through a backtracking strategy to produce top-down saliency. From a set of saliency maps of an image produced by fast bottom-up saliency approaches, we select the best saliency map suitable for the top-down task. The selected bottom-up saliency map is combined with the top-down saliency map. Features having high combined saliency are used to train a linear SVM classifier to estimate feature saliency. This is integrated with combined saliency and further refined through a multi-scale superpixel-averaging of saliency map. We evaluate the performance of the proposed weakly supervised topdown saliency and achieve comparable performance with fully supervised approaches. Experiments are carried out on seven challenging datasets and quantitative results are compared with 40 closely related approaches across 4 different applications. Code will be made publicly available.

Sampling-based alpha matting methods have traditionally followed the compositing equation to estimate the alpha value at a pixel from a pair of foreground (F) and background (B) samples. The (F,B) pair that produces the least reconstruction error is selected, followed by alpha estimation. The significance of that residual error has been left unexamined. In this letter, we propose a video matting algorithm that uses L1-regularized reconstruction error of F and B samples as a measure of the alpha matte. A multiframe nonlocal means framework using coherency sensitive hashing is utilized to ensure temporal coherency in the video mattes. Qualitative and quantitative evaluations on a dataset exclusively for video matting demonstrate the effectiveness of the proposed matting algorithm.

Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a supervised setting involving annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image. First, the probabilistic contribution of each image patch to the confidence of an ScSPM-based classifier produces a Reverse-ScSPM (R-ScSPM) saliency map. Neighborhood information is then incorporated through a contextual saliency map which is estimated using logistic regression learnt on patches having high R-ScSPM saliency. Both the saliency maps are combined to obtain the final saliency map. We evaluate the performance of the proposed weakly supervised top-down saliency and achieves comparable performance with fully supervised approaches. Experiments are carried out on 5 challenging datasets across 3 different applications.

Existing color sampling-based alpha matting methods use the compositing equation to estimate alpha at a pixel from the pairs of foreground ( F ) and background ( B ) samples. The quality of the matte depends on the selected ( F,B ) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to ( F,B ) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms the current stateoftheart in image and video matting.

We propose a sparse coding based framework for top-down salient object detection in which three locality constraints are integrated. First is the spatial or contextual lo- cality constraint in which features from adjacent regions have similar code, second is the feature-domain locality constraint in which similar features have similar code, and third is the category-domain locality constraint in which features are coded using similar atoms from each partition of the dictionary, where each partition corresponds to an object category. This faster coding strategy produces better saliency maps compared to conven- tional sparse coding. Proposed codes are max-pooled over a spatial neighborhood for saliency estimation. In spite of its simplicity, the proposed top-down saliency achieves state-of-the-art results at patch-level on two challenging datasets-Graz-02 and PASCAL VOC-07. A novel Gaussian-weighted interpolation further improves pixel-level saliency map derived from the patch-level map.

In digital matting, the availability of an accurate trimap is essential for pulling an accurate matte. This requirement becomes all the more critical in video matting where temporal coherence of the trimap is an added requirement. However, the task of manually drawing a trimap for every frame is not feasible. This paper proposes an adaptive trimap propagation framework that alleviates this task by automatically generating trimaps between two key-frames. 2-D shape blending is coupled with motion flow using a novel strategy based on angle criteria to generate accurate in-between foreground contours for the entire video sequence. A robust trimap generation step ensures temporal coherence between the contours. Quantitative and qualitative comparisons on complex video sequences demonstrates the effectiveness of the proposed method.