Object-Centric Anomaly Detection by Attribute-Based Reasoning
Babak Saleh, Ali Farhadi, Ahmed Elgammal
In proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
When describing images, humans tend not to talk about
the obvious, but rather mention what they find interesting.
We argue that abnormalities and deviations from typicalities
are among the most important components that form
what is worth mentioning. In this paper we introduce the
abnormality detection as a recognition problem and show
how to model typicalities and, consequently, meaningful
deviations from prototypical properties of categories. Our
model can recognize abnormalities and report the main reasons
of any recognized abnormality. We also show that abnormality
predictions can help image categorization. We
introduce the abnormality detection dataset and show interesting
results on how to reason about abnormalities.
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions
Mohamed Elhoseiny, Babak Saleh, Ahmed Elgammal
In proceedings of International Conference on Computer Vision (ICCV) 2013
The main question we address in this paper is how to use purely textual description of categories with no training
images to learn visual classifiers for these categories. We propose an approach for zero-shot learning of object categories
where the description of unseen categories comes in the form of typical text such as an encyclopedia entry,
without the need to explicitly defined attributes. We propose and investigate two baseline formulations, based on
regression and domain adaptation. Then, we propose a new constrained optimization formulation that combines a regression
function and a knowledge transfer function with additional constraints to predict the classifier parameters
for new classes. We applied the proposed approach on two fine-grained categorization datasets, and the results indicate
successful classifier prediction.
Heterogeneous Domain Adaptation: Learning Visual Classifiers from Textual Description
Mohamed Elhoseiny, Babak Saleh, Ahmed Elgammal
In Proceedings of the Workshop on Visual Domain Adaptation and Dataset Bias, In conjunction with ICCV'13'.
One of the main challenges for scaling up object recognition
systems is the lack of annotated images for real-world categories.
It is estimated that humans can recognize and discriminate among
about 30,000 categories. Typically there are few images available
for training classifiers form most of these categories. This is
reflected in the number of images per category available for training
in most object categorization datasets, shows a Zipf distribution.
The problem of lack of training images becomes even more
severe when we target recognition problems within a general category,
i.e., subordinate categorization, for example building classifiers
for different bird species or flower types (estimated over
10000 living bird species, similar for flowers). In this work we presented additional experiments to our ICCV paper.
Toward Automated Discovery of Artistic Influence
Babak Saleh, Kanako Abe, Ravneet Singh Arora, Ahmed Elgammal
International Journal of Multimedia Tools and Applications
Considering the huge amount of art pieces that exist, there
is valuable information to be discovered. Examining a painting,
an expert can determine its style, genre, and the time period that the painting belongs.
One important task for art historians is to nd influences and connections between
artists. Is influence a task that a computer can measure? The contribution
of this paper is in exploring the problem of computer-automated suggestion
uences between artists, a problem that was not addressed before in a
general setting. We rst present a comparative study of dierent classication
methodologies for the task of ne-art style classication. A two-level comparative study is performed for this classication problem. The rst level reviews
the performance of discriminative vs. generative models, while the second level
touches the features aspect of the paintings and compares semantic-level features vs low-level and intermediate level features present in the painting. Then,
we investigate the question "Who influenced this artist?" by looking at his
masterpieces and comparing them to others. We pose this interesting question
as a knowledge discovery problem. For this purpose, we investigated several
painting similarity and artist similarity measures. As a result, we provide a
visualization of artists (Map of Artists) based on the similarity between their
Knowledge Discovery of Artistic Influences: A Metric Learning Approach (Invited paper)
Babak Saleh, Kanako Abe, Ahmed Elgammal
International Conference on Computational Creativity (ICCC) 2014. (Oral presentation)
We approach the challenging problem of discovering influences
between painters based on their fine-art paintings.
In this work, we focus on comparing paintings of
two painters in terms of visual similarity. This comparison
is fully automatic and based on computer vision approaches
and machine learning. We investigated different
visual features and similarity measurements based
on two different metric learning algorithm to find the
most appropriate ones that follow artistic motifs. We
evaluated our approach by comparing its result with
ground truth annotation for a large collection of fine-art
An Early Framework for Determining Artistic Influence (Invited paper)
Kanako Abe, Babak Saleh, Ahmed Elgammal
In 2nd International Workshop on Multimedia for Cultural Heritage (MM4CH) 2013 (Oral presentation).
Considering the huge amount of art pieces that exist, there is
valuable information to be discovered. Focusing on paintings as one kind
of artistic creature that is printed on a surface, artists can determine its
genre and the time period that paintings can belong to. In this work we
are proposing the interesting problem of automatic in
uence determination between painters which has not been explored well. We answer the
question "Who influenced this artist?" by looking at his masterpieces
and comparing them to others. We pose this interesting question as a
knowledge discovery problem. We presented a novel dataset of paintings
for the interdisciplinary field of computer science and art and showed
interesting results for the task of influence finding.
Object Detection using Pictorial Structure of Gabor Template
Babak Saleh, Mohammad Rastegari
In proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2010.
Object detection methods are divided into two main branches: In the global approach one extracts low level
features and uses machine learning techniques. In the part-based approach one uses deformable templates. We
present a Hybrid approach for constructing a deformable template for modeling and detection. Initially one
applies Gabor wavelet filters to extract low level features and constructs graphs which resemble shock graphs.
A minimum spanning tree (MST) is extracted and is called the pictorial graph. It is used for matching. The
pictorial graph is suitable for preserving the visual appearance of the shape of the object and for accommodating
shape variances. In this hybrid approach we maintain the generality of the global and the efficiency of
part-based approaches. Our algorithm has been applied to a set of test cases and the result shows improved
performance as compared to standard object detection methods that do not rely on human intervention.