3,909 research outputs found
A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval
We introduce a shape descriptor that extracts keypoints from binary images and
automatically detects the salient ones among them. The proposed descriptor operates as
follows: First, the contours of the image are detected and an image transformation is used to
generate background information. Next, pixels of the transformed image that have specific
characteristics in their local areas are used to extract keypoints. Afterwards, the most salient
keypoints are automatically detected by filtering out redundant and sensitive ones. Finally,
a feature vector is calculated for each keypoint by using the distribution of contour points
in its local area. The proposed descriptor is evaluated using public datasets of silhouette
images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned
logos. Experimental results show that the proposed descriptor compares strongly against
state of the art methods, and that it is reliable when applied on challenging images such as
fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto
SAVASA project @ TRECVID 2012: interactive surveillance event detection
In this paper we describe our participation in the interactive surveillance event detection task at TRECVid 2012. The system we developed was comprised of individual classifiers brought together behind a simple video search interface that enabled users to select relevant segments based on down~sampled animated gifs. Two types of user -- `experts' and `end users' -- performed the evaluations. Due to time constraints we focussed on three events -- ObjectPut, PersonRuns and Pointing -- and two of the five available cameras (1 and 3). Results from the interactive runs as well as discussion of the performance of the underlying retrospective classifiers are presented
Structured learning of metric ensembles with application to person re-identification
Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person
Re-Identification With Metric Ensembles", at
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html.
arXiv admin note: text overlap with arXiv:1503.0154
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