2,814 research outputs found
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
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Discriminative localization is essential for fine-grained image
classification task, which devotes to recognizing hundreds of subcategories in
the same basic-level category. Reflecting on discriminative regions of objects,
key differences among different subcategories are subtle and local. Existing
methods generally adopt a two-stage learning framework: The first stage is to
localize the discriminative regions of objects, and the second is to encode the
discriminative features for training classifiers. However, these methods
generally have two limitations: (1) Separation of the two-stage learning is
time-consuming. (2) Dependence on object and parts annotations for
discriminative localization learning leads to heavily labor-consuming labeling.
It is highly challenging to address these two important limitations
simultaneously. Existing methods only focus on one of them. Therefore, this
paper proposes the discriminative localization approach via saliency-guided
Faster R-CNN to address the above two limitations at the same time, and our
main novelties and advantages are: (1) End-to-end network based on Faster R-CNN
is designed to simultaneously localize discriminative regions and encode
discriminative features, which accelerates classification speed. (2)
Saliency-guided localization learning is proposed to localize the
discriminative region automatically, avoiding labor-consuming labeling. Both
are jointly employed to simultaneously accelerate classification speed and
eliminate dependence on object and parts annotations. Comparing with the
state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach
achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201
Fine-graind Image Classification via Combining Vision and Language
Fine-grained image classification is a challenging task due to the large
intra-class variance and small inter-class variance, aiming at recognizing
hundreds of sub-categories belonging to the same basic-level category. Most
existing fine-grained image classification methods generally learn part
detection models to obtain the semantic parts for better classification
accuracy. Despite achieving promising results, these methods mainly have two
limitations: (1) not all the parts which obtained through the part detection
models are beneficial and indispensable for classification, and (2)
fine-grained image classification requires more detailed visual descriptions
which could not be provided by the part locations or attribute annotations. For
addressing the above two limitations, this paper proposes the two-stream model
combining vision and language (CVL) for learning latent semantic
representations. The vision stream learns deep representations from the
original visual information via deep convolutional neural network. The language
stream utilizes the natural language descriptions which could point out the
discriminative parts or characteristics for each image, and provides a flexible
and compact way of encoding the salient visual aspects for distinguishing
sub-categories. Since the two streams are complementary, combining the two
streams can further achieves better classification accuracy. Comparing with 12
state-of-the-art methods on the widely used CUB-200-2011 dataset for
fine-grained image classification, the experimental results demonstrate our CVL
approach achieves the best performance.Comment: 9 pages, to appear in CVPR 201
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