Abstract book (pdf) - ICPR 2010

For the interpretation of a visual scene, it is important for a robotic system to pay attention to the objects in the scene and
segment them from their background. We focus on the segmentation of previously unseen objects in unknown scenes. The
attention model therefore needs to be bottom-up and context-free. In this paper, we propose the use of symmetry, one of
the Gestalt principles for figure-ground segregation, to guide the robot’s attention. We show that our symmetry-saliency
model outperforms the contrast-saliency model, proposed in (Itti et al 1998). The symmetry model performs better in finding
the objects of interest and selects a fixation point closer to the center of the object. Moreover, the objects are better
segmented from the background when the initial points are selected on the basis of symmetry.
ThBT2 Anadolu Auditorium
Classification - II Regular Session
Session chair: Pelillo, Marcello (Ca’Foscari Univ.)
13:30-13:50, Paper ThBT2.1
Data Classification on Multiple Manifolds
Xiao, Rui, Shanghai Jiao Tong Univ.
Zhao, Qijun, The Hong Kong Pol. Univ.
Zhang, David, The Hong Kong Pol. Univ.
Shi, Pengfei, Shanghai Jiao Tong Univ.
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold,
we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore,
better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a
class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly
learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality
of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum
reconstruction error based classifier. Our method could be easily extended by involving various manifold learning
methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the
effectiveness of the proposed multiple manifold based approach.
13:50-14:10, Paper ThBT2.2
Unsupervised Ensemble Ranking: Application to Large-Scale Image Retrieval
Lee, Jung-Eun, Michigan State Univ.
Jin, Rong, Michigan State Univ.
Jain, Anil, Michigan State Univ.
The continued explosion in the growth of image and video databases makes automatic image search and retrieval an extremely
important problem. Among the various approaches to Content-based Image Retrieval (CBIR), image similarity
based on local point descriptors has shown promising performance. However, this approach suffers from the scalability
problem. Although bag-of-words model resolves the scalability problem, it suffers from loss in retrieval accuracy. We circumvent
this performance loss by an ensemble ranking approach in which rankings from multiple bag-of-words models
are combined to obtain more accurate retrieval results. An unsupervised algorithm is developed to learn the weights for
fusing the rankings from multiple bag-of-words models. Experimental results on a database of 100,000 images show that
this approach is both efficient and effective in finding visually similar images.
14:10-14:30, Paper ThBT2.3
Cross Entropy Optimization of the Random Set Framework for Multiple Instance Learning
Bolton, Jeremy, Univ. of Florida
Gader, Paul, Univ. of Florida
Multiple instance learning (MIL) is a recently researched technique used for learning a target concept in the presence of
noise. Previously, a random set framework for multiple instance learning (RSF-MIL) was proposed; however, the proposed
optimization strategy did not permit the harmonious optimization of model parameters. A cross entropy, based optimization
strategy is proposed. Experimental results on synthetic examples, benchmark and landmine data sets illustrate the benefits
of the proposed optimization strategy.
- 281 -