Babak Saleh

Ph.D. Student
Computer Science
Rutgers, The State University of New Jersey
email: babak*-at-c*-dot-rutger*-dot-edu {replace * with s}
address: 110 Frelinghuysen Rd, Piscataway NJ 08854
office: Hill center 257
phone: (732) 743-5154
portrait
short bio: I am a third year computer science Ph.D. student at Rutgers, The state university of New Jersey. I am a member of Computer Vision Group at CBIM, where I am advised by Dr. Ahmed Elgammal. I've had the privilage of collaboration with Dr. Ali Farhadi .
I completed my M.Sc. in Computer Science at Rutgers, The state university of New Jersey and hold a B.Sc. in Computer Science from Sharif University of Technology
My research revolves around "Computer Vision", "Machine Learning", "Computer Graphics", "Human Perception" and "Statistical Pattern Recognition".
You can find more information in my resume
 

Publications


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.
[PDF] [Project Page] [BibTeX] [Poster]

Abstract: 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
[PDF] [BibTeX] [Poster]

Abstract: 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'.
[PDF] [BibTeX] [Slides]

Abstract: 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
[PDF]

Abstract: 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 of in uences between artists, a problem that was not addressed before in a general setting. We rst present a comparative study of di erent classi cation methodologies for the task of ne-art style classi cation. A two-level comparative study is performed for this classi cation 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 work.

 

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)
[PDF] [BibTeX]

Abstract: 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 paintings.

 

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).
[PDF] [BibTeX]

Abstract: 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.
[PDF]

Abstract: 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.