Shyi-Chyi Cheng

A fast and novel technique for color quantization using reduction of color space dimensionality

Abstract This paper describes a fast and novel technique for color quantization using reduction of color space dimensionality. The color histogram is repeatedly sub-divided into smaller and smaller classes. The colors of each class are projected on a carefully selected line, such that the color dis-similarities are preserved. Instead of using the principal axis of each class, the line is defined by the mean color vector and the color of the largest distance away from the mean color. The vector composed of the projection values for each class is then used to cluster the colors into two representative palette colors. As a result, the computation in the quantization process is fast. A fast pixel mapping algorithm based on the proposed data clustering algorithm is also presented in this paper. Experimental results show that the proposed algorithms quantize images with high image quality efficiently.

3D model retrieval using principal plane analysis and dynamic programming

Three dimensional models play an important role in many applications; the problem is how to select the appropriate models from a 3D database rapidly and accurately. In recent years, a variety of shape representations, statistical methods, and geometric algorithms have been proposed for matching 3D shapes or models. In this paper, we propose a 3D shape representation scheme based on a combination of principal plane analysis and dynamic programming. The proposed 3D shape representation scheme consists of three steps. First, a 3D model is transformed into a 2D image by projecting the vertices of the model onto its principal plane. Second, the convex hall of the 2D shape of the model is further segmented into multiple disjoint triangles using dynamic programming. Finally, for each triangle, a projection score histogram and moments are extracted as the feature vectors for similarity searching. Experimental results showed the robustness of the proposed scheme, which resists translation, rotation, scaling, noise, and destructive attacks. The proposed 3D model retrieval method performs fairly well in retrieving models having similar characteristics from a database of 3D models.

Semantic-based facial expression recognition using analytical hierarchy process

In this paper we present an automatic facial expression recognition system that utilizes a semantic-based learning algorithm using the analytical hierarchy process (AHP). All the automatic facial expression recognition methods are similar in that they first extract some low-level features from the images or video, then these features are used as inputs into a classification system, and the outcome is one of the preselected emotion categories. Although the effectiveness of low-level features in automatic facial expression recognition systems has been widely studied, the success is shadowed by the innate discrepancy between the machine and human perception to the image. The gap between low-level visual features and high-level semantics should be bridged in a proper way in order to construct a seamless automatic facial expression system satisfying the user perception. For this purpose, we use the AHP to provide a systematical way to evaluate the fitness of a semantic description for interpreting the emotion of a face image. A semantic-based learning algorithm is also proposed to adapt the weights of low-level visual features for automatic facial expression recognition. The weights are chosen such that the discrepancy between the facial expression recognition results obtained in terms of low-level features and high-level semantic description is small. In the recognition phase, only the low-level features are used to classify the emotion of an input face image. The proposed semantic learning scheme provides a way to bridge the gap between the high-level semantic concept and the low-level features for automatic facial expression recognition. Experimental results show that the performance of the proposed method is excellent when it is compared with that of traditional facial expression recognition methods.

Subpixel edge detection of color images by principal axis analysis and moment-preserving principle

This paper discusses a new approach for detecting edges with sub-pixel accuracy in color images using the principal axis analysis and the moment-preserving principle. First, the principal axis of the color vectors in a three-dimensional space is obtained analytically for each image block. Based on the projection scores, the direction of the line edge is obtained by analyzing the three-dimensional principal axis of a color image block. Next, by preserving some spatial color moments of the pixels of each square block, analytic formulas are derived to extract the edge feature. Experiments on both synthetic and real images show that the accuracy of the proposed method is comparable to other color edge detectors. The proposed algorithm can be performed very fast for real-time applications with no need for special hardware.

Fast algorithms for color image processing by principal component analysis

Abstract This paper discusses a new approach for ordering color vectors by principal component analysis. A color image is represented by a vector field and the color vectors in an n by n pixel window are ordered according to the projection scores obtained by projecting each color vector within the window on the principal axis. We subtract each color vector in a window from the color vector of the central pixel before constructing the corresponding covariance matrix. For the purpose of computation efficiency, a fast approximation of the principal axis is also proposed. By applying the vector order statistics, various applications of color image processing, such as color image sharpening, color image compression, and color edge detection are also proposed in this paper. Therefore, the proposed color vector ordering method can be used as a tool for color image processing.

A semantic learning for content-based image retrieval using analytical hierarchy process

In this paper, a new semantic learning method for content-based image retrieval using the analytic hierarchical process (AHP) is proposed. AHP proposed by Satty used a systematical way to solve multi-criteria preference problems involving qualitative data and was widely applied to a great diversity of areas. In general, the interpretations of an image are multiple and hard to describe in terms of low-level features due to the lack of a complete image understanding model. The AHP provides a good way to evaluate the fitness of a semantic description used to interpret an image. According to a predefined concept hierarchy, a semantic vector, consisting of the fitness values of semantic descriptions of a given image, is used to represent the semantic content of the image. Based on the semantic vectors, the database images are clustered. For each semantic cluster, the weightings of the low-level features (i.e. color, shape, and texture) used to represent the content of the images are calculated by analyzing the homogeneity of the class. In this paper, the values of weightings setting to the three low-level feature types are diverse in different semantic clusters for retrieval. The proposed semantic learning scheme provides a way to bridge the gap between the high-level semantic concept and the low-level features for content-based image retrieval. Experimental results show that the performance of the proposed method is excellent when compared with that of the traditional text-based semantic retrieval techniques and content-based image retrieval methods.

Efficient adaptive error concealment technique for video decoding system

This paper presents a novel error concealment method for video decoding system. The proposed algorithm adaptively combines the spatial interpolation and the temporal prediction technique based on block variance and interframe correlation, to recover the lost data. The adaptive function depends on the scene change detection, motion distance and spatial information from the nearby blocks of the previous and current frames to determine the weighting of the spatial interpolation and the temporal compensation. Simulations demonstrate that the proposed technique can achieve well subjective and quantitative results, and outperforms all the others against which are compared. Even if the scene changes in the videos, this algorithm also can efficiently recover the damaged blocks for Intra(I), Predictive(P), and Bidirectional (B) frames.

Human Smoking Event Detection Using Visual Interaction Clues

This paper presents a novel scheme to automatically and directly detect smoking events in video. In this scheme, a color-based ratio histogram analysis is introduced to extract the visual clues from appearance interactions between lighted cigarette and its human holder. The techniques of color re-projection and Gaussian Mixture Models (GMMs) enable the tasks of cigarette segmentation and tracking over the background pixels. Then, a key problem for event analysis is the non-regular form of smoking events. Thus, we propose a self-determined mechanism to analyze this suspicious event using HHM framework. Due to the uncertainties of cigarette size and color, there is no automatic system which can well analyze human smoking events directly from videos. The proposed scheme is compatible to detect the smoking events of uncertain actions with various cigarette sizes, colors, and shapes, and has capacity to extend visual analysis to human events of similar interaction relationship. Experimental results show the effectiveness and real-time performances of our scheme in smoking event analysis.

Fusion of color edge detection and color quantization for color image watermarking using principal axes analysis

In the past few years, many gray-level image watermarking schemes have been proposed, although the extension to the color case is rare and regularly accomplished by marking the image luminance, or by processing each color channel separately. This paper presents a new color image watermarking scheme that combines color edge detection and color quantization using principal axes analysis in three-dimensional color space. The watermark is hidden within the data by modifying a subset of carefully selected edge points to resist both geometric distortion and signal processing attacks. Experimental results show the robustness of the proposed scheme to resist common attacks.

Content-based image retrieval using moment-preserving edge detection

Abstract A content-based image retrieval algorithm based on a new edge detection technique is proposed. Both the query and database images are divided into non-overlapping square blocks and coded by the mean in each uniform block and by edge information in each non-uniform block. The coded blocks of a query image are then used to find matches from an image database. The edge feature in a given block is detected by applying the moment-preserving principle to the image data. The edge directions are approximated by multiples of 45° to speed up the matching process without introducing obvious distortion. For a larger database, a selective filtering strategy based on the visual-pattern histograms is also described to further speed up the retrieval process. The solution to the edge detection problem in a given block is also analytic. This algorithm can be performed very fast for large database applications with no need for special hardware.