International Journal of Pattern Recognition and Artificial Intelligence

This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

In this paper we consider hexagonal arrays on triangular grids and introduce hexagonal local picture languages and hexagonal tiling systems defining hexagonal recognizable picture languages, motivated by an analogous study of rectangular arrays by Giammarresi and Restivo. We also introduce hexagonal Wang tiles to define hexagonal Wang systems (HWS) as a formalism to describe hexagonal picture languages. It is noticed that the family of hexagonal picture languages defined by hexagonal Wang systems and the family recognized by hexagonal tiling systems coincide. Analogous to hv-domino systems describing rectangular arrays, we define xyz-domino systems and prove that recognizable hexagonal picture languages are characterized as projections of xyz-local picture languages.

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I₁I₂I₃, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

Personalized recommendation needs powerful Web Intelligence (WI) technologies to manage, analyze and employ various business data on the Web for e-business intelligence. This paper presents a novel recommendation framework on the Web, which is based on a multilevel customer model comprising three submodels, namely, the customer shopping model (CSM), the customer preference model (CPM), and the customer consumption model (CCM). These models capture a customer's information from different aspects. After preprocessing of raw data, we first build the CSM based on Bayesian networks by mining from customer shopping transactions, and then find the CPM by analyzing customer shopping history. Furthermore, the customer purchasing power can be formalized as a linear CCM. By combining the CSM with the present customer shopping action, a recommendation algorithm based on Bayesian probability inference is used to generate an individual recommendation set of commodities. A personalized filter including customization of the CPM and orientation of the CCM is also used to realize a more personalized recommendation. Experimental evaluation on real world data shows that the proposed approach can achieve personalized commodities recommendation efficiently and effectively.

This paper presents a new regularization technique to deal with the small sample size (S3) problem in linear discriminant analysis (LDA) based face recognition. Regularization on the within-class scatter matrix S_w has been shown to be a good direction for solving the S3 problem because the solution is found in full space instead of a subspace. The main limitation in regularization is that a very high computation is required to determine the optimal parameters. In view of this limitation, this paper re-defines the three-parameter regularization on the within-class scatter matrix , which is suitable for parameter reduction. Based on the new definition of , we derive a single parameter (t) explicit expression formula for determining the three parameters and develop a one-parameter regularization on the within-class scatter matrix. A simple and efficient method is developed to determine the value of t. It is also proven that the new regularized within-class scatter matrix approaches the original within-class scatter matrix S_w as the single parameter tends to zero. A novel one-parameter regularization linear discriminant analysis (1PRLDA) algorithm is then developed. The proposed 1PRLDA method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. The average recognition accuracies of 50 runs for ORL and FERET databases are 96.65% and 94.00%, respectively. Comparing with existing LDA-based methods in solving the S3 problem, the proposed 1PRLDA method gives the best performance.

Vessels in retinal fundus images are useful in revealing the severity of eye-related diseases. In addition, they can act as landmarks for localizing lesions or the central vision area, and guide laser treatment of neovascularization. In this paper, we propose a two-stage scheme to extract vessels and reconstruct the morphological structure of vessels in retinal images. First, we employ mathematical morphology techniques to highlight large and small vessels with respect to their spatial properties. Different curvature response between vessel and noise patterns allows the use of curvature evaluation to remove enhanced vessel-like noise. A set of linear filters finalize the vessel map. However, the resulting vascular structure is incomplete of some important features in bifurcation points and central reflex. In order to rectify the pitfall, a reconstruction process is performed using dynamic local region growth to recover the morphological structure of vessels. Average performance of our method to extract vessels is 83.7% of TPR(True positive rate) and 3.8% of FPR(False positive rate) for 35 retinal images which include 21 abnormal images.