40,655 research outputs found
The Secrets of Salient Object Segmentation
In this paper we provide an extensive evaluation of fixation prediction and
salient object segmentation algorithms as well as statistics of major datasets.
Our analysis identifies serious design flaws of existing salient object
benchmarks, called the dataset design bias, by over emphasizing the
stereotypical concepts of saliency. The dataset design bias does not only
create the discomforting disconnection between fixations and salient object
segmentation, but also misleads the algorithm designing. Based on our analysis,
we propose a new high quality dataset that offers both fixation and salient
object segmentation ground-truth. With fixations and salient object being
presented simultaneously, we are able to bridge the gap between fixations and
salient objects, and propose a novel method for salient object segmentation.
Finally, we report significant benchmark progress on three existing datasets of
segmenting salient objectsComment: 15 pages, 8 figures. Conference version was accepted by CVPR 201
Learning Visual Attributes
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as āredā, āstripedā, or āspottedā. The model sees attributes as patterns of image segments, repeatedly sharing some characteristic properties. These can be any combination of appearance, shape, or the layout of segments within the pattern. Moreover, attributes with general appearance are taken into account, such as the pattern of alternation of any two colors which is characteristic for stripes. To enable learning from unsegmented training images, the model is learnt discriminatively, by optimizing a likelihood ratio. As demonstrated in the experimental evaluation, our model can learn in a weakly supervised setting and encompasses a broad range of attributes. We show that attributes can be learnt starting from a text query to Google image search, and can then be used to recognize the attribute and determine its spatial extent in novel real-world images.
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
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