Compact and Adaptive Spatial Pyramids for Object and Scene Recognition.
by Dr. Noha Elfiky
School of Economics and Business Administration at Saint Mary’s College of California.
Abstract: The release of challenging datasets with a vast number of images, requires the development of efficient image representations and algorithms which are able to manipulate these largescale datasets efficiently. Nowadays the Bag-of-Words (BoW) based image representation is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. The main SP approach, works by repeatedly sub-dividing the image into increasingly finer sub-regions by doubling the number of divisions on each axis direction, and further computing histograms of features over the resulting sub-regions. Observing the remarkable performance of spatial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation still remains an active research domain.
In summary, the main concern of this talk is to highlight the limits of spatial pyramids and try to figure out solutions for them. This talk explores the problem of obtaining compact, adaptive, yet informative spatial image representations in the context of object and scene classification tasks.
Noha Elfiky is an Assistant Professor in the School of Economics and Business Administration at Saint Mary’s College of California. She is also the Director of the Master of Science in Management and Technology program at Saint Mary’s College of California. She received a Ph.D. and M.S. in Computer Vision and Artificial Intelligence from Universitat Autònoma de Barcelona (UAB), Barcelona, Spain, and a B.S. in Computer Science and Information Systems from Ain Shams University, Egypt. She made her post doctorates studies at the School of Electrical and Computer Engineering, Purdue University. Her current research interests are in Machine Learning, Artificial Intelligence, Computer Vision, and Business & Data Analytics.