56 research outputs found
Learning Dictionary of Discriminative Part Detectors for Image Categorization and Cosegmentation
International audienceThis paper proposes a novel approach to learning mid-level image models for image categorization and cosegmentation. We represent each image class by a dictionary of discriminative part detectors that best discriminate that class from the background. We learn category-specific part detectors in a weakly supervised setting in which the training images are only labeled with category labels without part / object location labels. We use a latent SVM model regularized by l1,2 group sparsity to learn the discriminative part detectors. Starting from a large set of initial parts, the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max-margin framework. We propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem. We apply the learned part detectors to image classification and cosegmentation, and quantitative experiments with standard benchmarks show that our approach matches or improves upon the state of the art
Learning Discriminative Part Detectors for Image Classification and Cosegmentation
International audienceIn this paper, we address the problem of learning discriminative part detectors from image sets with category labels. We propose a novel latent SVM model regularized by group sparsity to learn these part detectors. Starting from a large set of initial parts, the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max-margin framework. We propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem. We apply the proposed method to image classification and cosegmentation, and quantitative experiments with standard benchmarks show that it matches or improves upon the state of the art
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
Indoor scene recognition is a multi-faceted and challenging problem due to
the diverse intra-class variations and the confusing inter-class similarities.
This paper presents a novel approach which exploits rich mid-level
convolutional features to categorize indoor scenes. Traditionally used
convolutional features preserve the global spatial structure, which is a
desirable property for general object recognition. However, we argue that this
structuredness is not much helpful when we have large variations in scene
layouts, e.g., in indoor scenes. We propose to transform the structured
convolutional activations to another highly discriminative feature space. The
representation in the transformed space not only incorporates the
discriminative aspects of the target dataset, but it also encodes the features
in terms of the general object categories that are present in indoor scenes. To
this end, we introduce a new large-scale dataset of 1300 object categories
which are commonly present in indoor scenes. Our proposed approach achieves a
significant performance boost over previous state of the art approaches on five
major scene classification datasets
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.Comment: 14 pages, 7 figure
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