11,858 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
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
This paper addresses unsupervised discovery and localization of dominant
objects from a noisy image collection with multiple object classes. The setting
of this problem is fully unsupervised, without even image-level annotations or
any assumption of a single dominant class. This is far more general than
typical colocalization, cosegmentation, or weakly-supervised localization
tasks. We tackle the discovery and localization problem using a part-based
region matching approach: We use off-the-shelf region proposals to form a set
of candidate bounding boxes for objects and object parts. These regions are
efficiently matched across images using a probabilistic Hough transform that
evaluates the confidence for each candidate correspondence considering both
appearance and spatial consistency. Dominant objects are discovered and
localized by comparing the scores of candidate regions and selecting those that
stand out over other regions containing them. Extensive experimental
evaluations on standard benchmarks demonstrate that the proposed approach
significantly outperforms the current state of the art in colocalization, and
achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Deep learning has shown state-of-art classification performance on datasets
such as ImageNet, which contain a single object in each image. However,
multi-object classification is far more challenging. We present a unified
framework which leverages the strengths of multiple machine learning methods,
viz deep learning, probabilistic models and kernel methods to obtain
state-of-art performance on Microsoft COCO, consisting of non-iconic images. We
incorporate contextual information in natural images through a conditional
latent tree probabilistic model (CLTM), where the object co-occurrences are
conditioned on the extracted fc7 features from pre-trained Imagenet CNN as
input. We learn the CLTM tree structure using conditional pairwise
probabilities for object co-occurrences, estimated through kernel methods, and
we learn its node and edge potentials by training a new 3-layer neural network,
which takes fc7 features as input. Object classification is carried out via
inference on the learnt conditional tree model, and we obtain significant gain
in precision-recall and F-measures on MS-COCO, especially for difficult object
categories. Moreover, the latent variables in the CLTM capture scene
information: the images with top activations for a latent node have common
themes such as being a grasslands or a food scene, and on on. In addition, we
show that a simple k-means clustering of the inferred latent nodes alone
significantly improves scene classification performance on the MIT-Indoor
dataset, without the need for any retraining, and without using scene labels
during training. Thus, we present a unified framework for multi-object
classification and unsupervised scene understanding
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
Given a set of images containing objects from the same category, the task of
image co-localization is to identify and localize each instance. This paper
shows that this problem can be solved by a simple but intriguing idea, that is,
a common object detector can be learnt by making its detection confidence
scores distributed like those of a strongly supervised detector. More
specifically, we observe that given a set of object proposals extracted from an
image that contains the object of interest, an accurate strongly supervised
object detector should give high scores to only a small minority of proposals,
and low scores to most of them. Thus, we devise an entropy-based objective
function to enforce the above property when learning the common object
detector. Once the detector is learnt, we resort to a segmentation approach to
refine the localization. We show that despite its simplicity, our approach
outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201
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