2,521 research outputs found
Video Saliency Detection Using Object Proposals
In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg-Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms
Automatic Action Annotation in Weakly Labeled Videos
Manual spatio-temporal annotation of human action in videos is laborious,
requires several annotators and contains human biases. In this paper, we
present a weakly supervised approach to automatically obtain spatio-temporal
annotations of an actor in action videos. We first obtain a large number of
action proposals in each video. To capture a few most representative action
proposals in each video and evade processing thousands of them, we rank them
using optical flow and saliency in a 3D-MRF based framework and select a few
proposals using MAP based proposal subset selection method. We demonstrate that
this ranking preserves the high quality action proposals. Several such
proposals are generated for each video of the same action. Our next challenge
is to iteratively select one proposal from each video so that all proposals are
globally consistent. We formulate this as Generalized Maximum Clique Graph
problem using shape, global and fine grained similarity of proposals across the
videos. The output of our method is the most action representative proposals
from each video. Our method can also annotate multiple instances of the same
action in a video. We have validated our approach on three challenging action
datasets: UCF Sport, sub-JHMDB and THUMOS'13 and have obtained promising
results compared to several baseline methods. Moreover, on UCF Sports, we
demonstrate that action classifiers trained on these automatically obtained
spatio-temporal annotations have comparable performance to the classifiers
trained on ground truth annotation
Object Discovery via Cohesion Measurement
Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However, image pixels, which share a similar real-world color, may
be quite different since colors are often distorted by intensity. In this
paper, we reinvestigate the affinity matrices originally used in image
segmentation methods based on spectral clustering. A new affinity matrix, which
is robust to color distortions, is formulated for object discovery. Moreover, a
Cohesion Measurement (CM) for object regions is also derived based on the
formulated affinity matrix. Based on the new Cohesion Measurement, a novel
object discovery method is proposed to discover objects latent in an image by
utilizing the eigenvectors of the affinity matrix. Then we apply the proposed
method to both saliency detection and object proposal generation. Experimental
results on several evaluation benchmarks demonstrate that the proposed CM based
method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure
Automatic annotation for weakly supervised learning of detectors
PhDObject detection in images and action detection in videos are among the most widely studied
computer vision problems, with applications in consumer photography, surveillance, and automatic
media tagging. Typically, these standard detectors are fully supervised, that is they require
a large body of training data where the locations of the objects/actions in images/videos have
been manually annotated. With the emergence of digital media, and the rise of high-speed internet,
raw images and video are available for little to no cost. However, the manual annotation
of object and action locations remains tedious, slow, and expensive. As a result there has been
a great interest in training detectors with weak supervision where only the presence or absence
of object/action in image/video is needed, not the location. This thesis presents approaches for
weakly supervised learning of object/action detectors with a focus on automatically annotating
object and action locations in images/videos using only binary weak labels indicating the presence
or absence of object/action in images/videos.
First, a framework for weakly supervised learning of object detectors in images is presented.
In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically
annotating object locations in weakly labelled data is presented which, unlike existing
approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial
annotation is then used to start an iterative process in which standard object detectors are used to
refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift
from the object of interest, a scheme for detecting model drift is also presented. Furthermore,
unlike most other methods, our weakly supervised approach is evaluated on data without manual
pose (object orientation) annotation.
Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues,
is carried out. From the analysis, a new method based on negative mining (NegMine) is presented
for the initial annotation of both object and action data. The NegMine based approach is a
much simpler formulation using only inter-class measure and requires no complex combinatorial
optimisation but can still meet or outperform existing approaches including the previously pre3
sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing
approaches to boost their performance.
Finally, the thesis will take a step back and look at the use of generic object detectors as prior
knowledge in weakly supervised learning of object detectors. These generic object detectors are
typically based on sampling saliency maps that indicate if a pixel belongs to the background
or foreground. A new approach to generating saliency maps is presented that, unlike existing
approaches, looks beyond the current image of interest and into images similar to the current
image. We show that our generic object proposal method can be used by itself to annotate the
weakly labelled object data with surprisingly high accuracy
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