International Journal of Image and Graphics

We propose a Multiple Class Multiple-Instance (MCMI) learning approach and demonstrate its application to the problem of image categorization. Our method extends the binary Multiple-Instance learning approach for image categorization. Instead of constructing a set of binary classifiers (each trained to separate one category from the rest) and then making the final decision based on the winner of all the binary classifiers, our method directly allows the computation of a multi-class classifier by first projecting each training image onto a multi-class feature space and then simultaneously minimizing the multi-class objective function in a Support Vector Machine framework. The multi-class feature space is constructed based on the instance prototypes obtained by Multiple-Instance learning which treats an image as a set of instances with training labels being associated with images rather than instances. The experiment results on two challenging data sets demonstrate that our method achieved better classification accuracy and is less sensitive to the training sample size compared with traditional one-versus-the-rest binary MI classification methods.

Coded structured light is an active approach that obtains the shape of object. In our previous work, we proposed a new binary projection mechanism for inspecting semiconductor products. The mechanism consists of only a single light source in combination with a binary grating for projecting binary pattern. By shifting the binary grating in space and in every shifting taking a separate image of the illuminated surface, each position on the illuminated surface is attached with a unique binary string for correspondence. In the mechanism the inspection speed is governed by the number of needed images which also equals to the number of shiftings of the grating. To reduce image number, we propose that two neighbor bits are combined together to produce unique codeword, which is referred to as bit-pairing mechanism. This paper addresses how the spatial shifting of bit-pairing mechanism can be designed optimally for minimizing this number for faster inspection speed. An optimal solution to shifting strategy optimization is proposed that is applicable to any given binary patterns. Theoretical analysis and real image experiments are presented to illustrate the workability of the solutions.

Face aging is a challenging work in image processing and computer animation field. In this paper, a novel local image Laplacian differential method is proposed to learn this complex face transformation. Firstly, together with the facial feature points correspondence, we extract the aging facial details from example face and apply to the target one by a global and a local Laplacian skin aging model respectively, and the latter makes the results more realistic in our experiment. Then, the skin color and hair style adjustments are carried out to simulate the aging process completely, making the results look more competitive and natural. Our experiment shows that our method is robust and appreciable.

It is an important problem nowadays to ensure the security of encrypted multimedia contents on the Internet. In this paper, a new blind attack procedure called NZCA is proposed to decrypt images that are encrypted by DCT-based methods. It threatens almost all existing DCT-based image encryption methods without the need of knowing the encryption algorithm details. The parameters of the NZCA can be adaptively selected via unsupervised learning with spectrum analysis of image projection. The effectiveness of the NZCA has been quantitatively evaluated by comparing the decrypted images with the edge sketches of original images. Our experiments on a number of real-world images show that NZCA is very powerful. Furthermore, we point out that a special form of inter-block shuffle, full inter-block shuffle (FIBS), is immune to the NZCA. Hence we suggest that all DCT-based image encryption methods for JPEG, MPEG, H.26x, etc. employ the FIBS so as to enhance the security.

In this paper, we present a novel noise suppression and detail preservation algorithm. The test image is firstly pre-processed through a multiresolution analysis employing the discrete wavelet transform. Then, we apply a fast and robust total variation technique, incorporating a statistical representation in the style of maximum likelihood estimation. Finally, we compare this proposed approach to current state-of-the-art denoising methods using synthetic and real images. The results demonstrate encouraging performance of our algorithm.

In this paper a novel restoration algorithm based on iterative loops and data mining is proposed to restore the original object image from a few frames of aero-optics degraded images through the turbulence medium. Based on the iterative loops, the iterative mathematic models to estimate the random turbulent optical point spread functions and object image are built. A series of restoration experiments for both simulated and real aero-optics degraded images were performed to examine the proposed algorithm under a computerized simulation environment, which show that the proposed algorithm outperform previous algorithms and therefore is effective for practical applications.

The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.

This article is a comprehensive evaluation of a new framework for indexing image data, called CMVF, which can combine multiple data properties with a hybrid architecture. The goal of this system is to allow straightforward incorporation of multiple visual feature vectors, based on properties such as color, texture and shape, into a single low-dimension vector that is more effective for retrieval than the larger individual feature vectors. Moreover, CMVF is not only constrained to visual properties, but can also incorporate human classification criteria to further strengthen image retrieval process. The controlled study present in this paper concentrates on CMVF's performance on images, examining how the incorporation of extra features into the indexing affects both efficiency and effectiveness. Analysis and empirical evidence suggest that the inclusion of extra visual features can significantly improve system performance. Furthermore, it demonstrated that CMVF's effectiveness is robust against various kinds of common image distortions and initial (random) configuration of neural network.

In this paper, we propose a new supervised learning algorithm, which is named the Generalized Marginal Fisher Analysis (GMFA), to utilize the advantages of the Marginal Fisher Analysis (MFA) and the Generalized Singular Value Decomposition (GSVD) techniques for face recognition. The experimental results on several standard face databases demonstrate that GMFA outperforms LDA/Fisherface, LDA/GSVD and MFA.