34,490 research outputs found
Semantic Part Segmentation using Compositional Model combining Shape and Appearance
In this paper, we study the problem of semantic part segmentation for
animals. This is more challenging than standard object detection, object
segmentation and pose estimation tasks because semantic parts of animals often
have similar appearance and highly varying shapes. To tackle these challenges,
we build a mixture of compositional models to represent the object boundary and
the boundaries of semantic parts. And we incorporate edge, appearance, and
semantic part cues into the compositional model. Given part-level segmentation
annotation, we develop a novel algorithm to learn a mixture of compositional
models under various poses and viewpoints for certain animal classes.
Furthermore, a linear complexity algorithm is offered for efficient inference
of the compositional model using dynamic programming. We evaluate our method
for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has
pixelwise part labels. Experimental results demonstrate the effectiveness of
our method
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding
Recent trends in image understanding have pushed for holistic scene
understanding models that jointly reason about various tasks such as object
detection, scene recognition, shape analysis, contextual reasoning, and local
appearance based classifiers. In this work, we are interested in understanding
the roles of these different tasks in improved scene understanding, in
particular semantic segmentation, object detection and scene recognition.
Towards this goal, we "plug-in" human subjects for each of the various
components in a state-of-the-art conditional random field model. Comparisons
among various hybrid human-machine CRFs give us indications of how much "head
room" there is to improve scene understanding by focusing research efforts on
various individual tasks
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to discover the class-agnostic salient regions, obtained
using a salient object detector, which otherwise would be ignored. Second, we
use fully convolutional attention maps to reliably localize the class-specific
regions in a given image. We combine these two cues to discover class-specific
pixels which are then used as an approximate ground truth for training a CNN.
While solving the weakly supervised semantic segmentation task, we ensure that
the image-level classification task is also solved in order to enforce the CNN
to assign at least one pixel to each object present in the image.
Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of
60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to
the published state-of-the-art results. The code is made publicly available
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