Abstract. This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile robotics and animats. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. We present some preliminary results obtained using synthetic data and also consider practical issues as convergence properties discuss future developments.

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The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to some robotic vision systems, like automatic object recognition and landmark detection. However, these kinds of applications have different requirements from those originally proposed. More specifically, a robotic vision system must be relatively insensitive to 2D similarity transformations of the image, as in-plane translations, rotations, reflections, and scales. In this paper several experiments with two visual attention models publicly available are described. The results show that the best known model, called NVT, is extremely sensitive to these 2D similarity transformations. Therefore, a new visual attention model, called NLOOK, is proposed and validated with the same invariance criteria, and the results show that NLOOK is less sensitive to these kind of transformations than the other two models. Besides, NLOOK can select better fixations according to a redundancy criterion. Thus, the proposed model is an excellent tool to be used in robot vision systems.