630 research outputs found
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.Comment: ICIP201
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
Geospatial object detection of remote sensing imagery has been attracting an
increasing interest in recent years, due to the rapid development in spaceborne
imaging. Most of previously proposed object detectors are very sensitive to
object deformations, such as scaling and rotation. To this end, we propose a
novel and efficient framework for geospatial object detection in this letter,
called Fourier-based rotation-invariant feature boosting (FRIFB). A
Fourier-based rotation-invariant feature is first generated in polar
coordinate. Then, the extracted features can be further structurally refined
using aggregate channel features. This leads to a faster feature computation
and more robust feature representation, which is good fitting for the coming
boosting learning. Finally, in the test phase, we achieve a fast pyramid
feature extraction by estimating a scale factor instead of directly collecting
all features from image pyramid. Extensive experiments are conducted on two
subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness
of the FRIFB compared to previous state-of-the-art methods
Indexing ensembles of exemplar-SVMs with rejecting taxonomies
Ensembles of Exemplar-SVMs have been used for a wide variety of tasks, such as object detection, segmentation, label transfer and mid-level feature learning. In order to make this technique effective though a large collection of classifiers is needed, which often makes the evaluation phase prohibitive. To overcome this issue we exploit the joint distribution of exemplar classifier scores to build a taxonomy capable of indexing each Exemplar-SVM and enabling a fast evaluation of the whole ensemble. We experiment with the Pascal 2007 benchmark on the task of object detection and on a simple segmentation task, in order to verify the robustness of our indexing data structure with reference to the standard Ensemble. We also introduce a rejection strategy to discard not relevant image patches for a more efficient access to the data
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
Computer vision tasks are traditionally defined and evaluated using semantic
categories. However, it is known to the field that semantic classes do not
necessarily correspond to a unique visual class (e.g. inside and outside of a
car). Furthermore, many of the feasible learning techniques at hand cannot
model a visual class which appears consistent to the human eye. These problems
have motivated the use of 1) Unsupervised or supervised clustering as a
preprocessing step to identify the visual subclasses to be used in a
mixture-of-experts learning regime. 2) Felzenszwalb et al. part model and other
works model mixture assignment with latent variables which is optimized during
learning 3) Highly non-linear classifiers which are inherently capable of
modelling multi-modal input space but are inefficient at the test time. In this
work, we promote an incremental view over the recognition of semantic classes
with varied appearances. We propose an optimization technique which
incrementally finds maximal visual subclasses in a regularized risk
minimization framework. Our proposed approach unifies the clustering and
classification steps in a single algorithm. The importance of this approach is
its compliance with the classification via the fact that it does not need to
know about the number of clusters, the representation and similarity measures
used in pre-processing clustering methods a priori. Following this approach we
show both qualitatively and quantitatively significant results. We show that
the visual subclasses demonstrate a long tail distribution. Finally, we show
that state of the art object detection methods (e.g. DPM) are unable to use the
tails of this distribution comprising 50\% of the training samples. In fact we
show that DPM performance slightly increases on average by the removal of this
half of the data.Comment: Updated ICCV 2013 submissio
Clothing Co-Parsing by Joint Image Segmentation and Labeling
This paper aims at developing an integrated system of clothing co-parsing, in
order to jointly parse a set of clothing images (unsegmented but annotated with
tags) into semantic configurations. We propose a data-driven framework
consisting of two phases of inference. The first phase, referred as "image
co-segmentation", iterates to extract consistent regions on images and jointly
refines the regions over all images by employing the exemplar-SVM (E-SVM)
technique [23]. In the second phase (i.e. "region co-labeling"), we construct a
multi-image graphical model by taking the segmented regions as vertices, and
incorporate several contexts of clothing configuration (e.g., item location and
mutual interactions). The joint label assignment can be solved using the
efficient Graph Cuts algorithm. In addition to evaluate our framework on the
Fashionista dataset [30], we construct a dataset called CCP consisting of 2098
high-resolution street fashion photos to demonstrate the performance of our
system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89%
recognition rate on the Fashionista and the CCP datasets, respectively, which
are superior compared with state-of-the-art methods.Comment: 8 pages, 5 figures, CVPR 201
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