128,767 research outputs found
Bayesian Modeling of Dynamic Scenes for Object Detection
Abstract—Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes. Index Terms—Object detection, kernel density estimation, joint domain range, MAP-MRF estimation. æ
Active Object Localization in Visual Situations
We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images.Comment: 14 page
Semantic Image Retrieval via Active Grounding of Visual Situations
We describe a novel architecture for semantic image retrieval---in
particular, retrieval of instances of visual situations. Visual situations are
concepts such as "a boxing match," "walking the dog," "a crowd waiting for a
bus," or "a game of ping-pong," whose instantiations in images are linked more
by their common spatial and semantic structure than by low-level visual
similarity. Given a query situation description, our architecture---called
Situate---learns models capturing the visual features of expected objects as
well the expected spatial configuration of relationships among objects. Given a
new image, Situate uses these models in an attempt to ground (i.e., to create a
bounding box locating) each expected component of the situation in the image
via an active search procedure. Situate uses the resulting grounding to compute
a score indicating the degree to which the new image is judged to contain an
instance of the situation. Such scores can be used to rank images in a
collection as part of a retrieval system. In the preliminary study described
here, we demonstrate the promise of this system by comparing Situate's
performance with that of two baseline methods, as well as with a related
semantic image-retrieval system based on "scene graphs.
Analysis of a biologically-inspired system for real-time object recognition
We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system
Iterative Object and Part Transfer for Fine-Grained Recognition
The aim of fine-grained recognition is to identify sub-ordinate categories in
images like different species of birds. Existing works have confirmed that, in
order to capture the subtle differences across the categories, automatic
localization of objects and parts is critical. Most approaches for object and
part localization relied on the bottom-up pipeline, where thousands of region
proposals are generated and then filtered by pre-trained object/part models.
This is computationally expensive and not scalable once the number of
objects/parts becomes large. In this paper, we propose a nonparametric
data-driven method for object and part localization. Given an unlabeled test
image, our approach transfers annotations from a few similar images retrieved
in the training set. In particular, we propose an iterative transfer strategy
that gradually refine the predicted bounding boxes. Based on the located
objects and parts, deep convolutional features are extracted for recognition.
We evaluate our approach on the widely-used CUB200-2011 dataset and a new and
large dataset called Birdsnap. On both datasets, we achieve better results than
many state-of-the-art approaches, including a few using oracle (manually
annotated) bounding boxes in the test images.Comment: To appear in ICME 2017 as an oral pape
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