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Effective resizing of images should not only use geometric constraints, but consider the image content as well. We present a simple image operator called <i>seam carving</i> that supports content-aware image resizing for both reduction and expansion. A seam is an optimal 8-connected path of pixels on a <i>single</i> image from top to bottom, or left to(More)
We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks,(More)
Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we replace the dynamic programming method of seam carving with graph cuts that are suitable for 3D volumes. In the(More)
  • Shai Avidan
  • IEEE Transactions on Pattern Analysis and Machine…
  • 2005
We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pixels in the next frame as either belonging to the object or(More)
  • Shai Avidan
  • IEEE Transactions on Pattern Analysis and Machine…
  • 2001
Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a(More)
Content aware resizing gained popularity lately and users can now choose from a battery of methods to retarget their media. However, no single retargeting operator performs well on all images and all target sizes. In a user study we conducted, we found that users prefer to combine seam carving with cropping and scaling to produce results they are satisfied(More)
Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the target. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the target variations over time. We then rigorously show that this model is(More)
We explore the problem of reconstructing an image from a bag of square, non-overlapping image patches, the jigsaw puzzle problem. Completing jigsaw puzzles is challenging and requires expertise even for humans, and is known to be NP-complete. We depart from previous methods that treat the problem as a constraint satisfaction problem and develop a graphical(More)
Coherency Sensitive Hashing (CSH) extends Locality Sensitivity Hashing (LSH) and PatchMatch to quickly find matching patches between two images. LSH relies on hashing, which maps similar patches to the same bin, in order to find matching patches. PatchMatch, on the other hand, relies on the observation that images are coherent, to propagate good matches to(More)
One fundamental problem in object retrieval with the bag-of-visual words (BoW) model is its lack of spatial information. Although various approaches are proposed to incorporate spatial constraints into the BoW model, most of them are either too strict or too loose so that they are only effective in limited cases. We propose a new spatially-constrained(More)