28,289 research outputs found
Learning Object Categories From Internet Image Searches
In this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models βon-the-fly.β We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general
setting where only a single unlabeled image is given. This is far more
challenge than typical co-localization or weakly-supervised localization tasks.
To tackle this problem, we propose a simple but effective pattern mining-based
method, called Object Location Mining (OLM), which exploits the advantages of
data mining and feature representation of pre-trained convolutional neural
networks (CNNs). Specifically, we first convert the feature maps from a
pre-trained CNN model into a set of transactions, and then discovers frequent
patterns from transaction database through pattern mining techniques. We
observe that those discovered patterns, i.e., co-occurrence highlighted
regions, typically hold appearance and spatial consistency. Motivated by this
observation, we can easily discover and localize possible objects by merging
relevant meaningful patterns. Extensive experiments on a variety of benchmarks
demonstrate that OLM achieves competitive localization performance compared
with the state-of-the-art methods. We also evaluate our approach compared with
unsupervised saliency detection methods and achieves competitive results on
seven benchmark datasets. Moreover, we conduct experiments on fine-grained
classification to show that our proposed method can locate the entire object
and parts accurately, which can benefit to improving the classification results
significantly
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
Detecting Visual Relationships with Deep Relational Networks
Relationships among objects play a crucial role in image understanding.
Despite the great success of deep learning techniques in recognizing individual
objects, reasoning about the relationships among objects remains a challenging
task. Previous methods often treat this as a classification problem,
considering each type of relationship (e.g. "ride") or each distinct visual
phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with
significant difficulties caused by the high diversity of visual appearance for
each kind of relationships or the large number of distinct visual phrases. We
propose an integrated framework to tackle this problem. At the heart of this
framework is the Deep Relational Network, a novel formulation designed
specifically for exploiting the statistical dependencies between objects and
their relationships. On two large datasets, the proposed method achieves
substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape
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