Yung-Kuan Chan

A smart content-based image retrieval system based on color and texture feature

In this paper, three image features are proposed for image retrieval. In addition, a feature selection technique is also brought forward to select optimal features to not only maximize the detection rate but also simplify the computation of image retrieval. The first and second image features are based on color and texture features, respectively called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP) in this paper. The third image feature is based on color distribution, called color histogram for K-mean (CHKM). CCM is the conventional pattern co-occurrence matrix that calculates the probability of the occurrence of same pixel color between each pixel and its adjacent ones in each image, and this probability is considered as the attribute of the image. According to the sequence of motifs of scan patterns, DBPSP calculates the difference between pixels and converts it into the probability of occurrence on the entire image. Each pixel color in an image is then replaced by one color in the common color palette that is most similar to color so as to classify all pixels in image into k-cluster, called the CHKM feature. Difference in image properties and contents indicates that different features are contained. Some images have stronger color and texture features, while others are more sensitive to color and spatial features. Thus, this study integrates CCM, DBPSP, and CHKM to facilitate image retrieval. To enhance image detection rate and simplify computation of image retrieval, sequential forward selection is adopted for feature selection. Besides, based on the image retrieval system (CTCHIRS), a series of analyses and comparisons are performed in our experiment. Three image databases with different properties are used to carry out feature selection. Optimal features are selected from original features to enhance the detection rate.

Block image retrieval based on a compressed linear quadtree

Based on a compressed linear quad tree, a data structure is proposed which is more than a structure to compress a gray-level or color image, but a structure, which can be applied to retrieve a block image. This compressed linear quadtree may directly extract a detailed block image without decompressing the compressed data of the original image.

A ROI image retrieval method based on CVAAO

A novel image feature called color variances among adjacent objects (CVAAO) is proposed in this study. Characterizing the color variances between contiguous objects in an image, CVAAO can effectively describe the principal colors and texture distribution of the image and is insensitive to distortion and scale variations of images. Based on CVAAO, a CVAAO-based image retrieval method is constructed. When given a full image, the CVAAO-based image retrieval method delivers the database images most similar to the full image to the user. This paper also presents a CVAAO-based ROI image retrieval method. When given a clip, the CVAAO-based ROI image retrieval method submits to the user a database image containing a target region most similar to the clip. The experimental results show that the CVAAO-based ROI image retrieval method can offer impressive results in finding out the database images that meet user requirements.

Image matching using run-length feature

Abstract Color histogram is the most commonly used color feature in image retrieval systems. However, this feature cannot effectively characterize an image, since it only captures the global properties. To make the retrieval more accurate, this paper introduces a run-length (RL) feature. The feature integrates the information of color and shape of the objects in an image. It can effectively discriminate the directions, areas and geometrical shapes of the objects. Yet, extracting the RL feature is time-consuming. For that reason, this paper also provides one revised representation of the RL, called semi-run-length (SRL). Based on the SRL feature, this paper develops an image retrieval system, and the experimental results show that the system gives an impressive performance.

Concealing a secret image using the breadth first traversal linear quadtree structure

This paper presents an image hiding method which embeds the secret image in the least significant bits (LSB) of the pixels selected from a cover image. Generally, LSB-related methods are rather sensitive to the modifications on the cover image. To abate the damage to the reconstructed cover image, this proposed method hence embeds the vital data of the cover image in more significant bits. This paper also provides a breadth first traversal (BFT) linear quadtree representation to characterize a compressed binary image. The data in the front of this structure are vital data; yet, those in the rear are the trivial ones. The strategy of this representation does facilitate image embedding.

A COLOR IMAGE RETRIEVAL METHOD BASED ON COLOR MOMENT AND COLOR VARIANCE OF ADJACENT PIXELS

This paper first introduces three simple and effective image features — the color moment (CM), the color variance of adjacent pixels (CVAP) and CM–CVAP. The CM feature delineates the color-spatial information of images, and the CVAP feature describes the color variance of pixels in an image. However, these two features can only characterize the content of images in different ways. This paper hence provides another feature CM–CVAP, which combines both, to raise the quality of similarity measure. The experimental results show that the image retrieval method based on the CM–CVAP feature gives quite an impressive performance.

Brief Communication: Design of multiplex PCR primers using heuristic algorithm for sequential deletion applications

UNLABELLEDnThe sequential deletion method is commonly applied to locate the functional domain of a protein. Unfortunately, manually designing primers for multiplex polymerase chain reaction (PCR) is a labor-intensive task. In order to speed up the experimental procedure and to improve the efficiency of producing PCR products, this paper proposes a multiplex PCR primers (MPCRPs) designer to design multiple forward primers with a single 3-UTR reverse primer for extracting various N-terminal truncated mutants to quickly locate the functional domain of a cDNA sequence. Several factors, including melting temperature, primer length, GC content, internal self-complement, cross-dimerization, terminal limitation, and specificity, are used as the criteria for designing primers. This study obtains a near-optimal solution of primer sets that can be placed in as few test tubes as possible for one multiplex PCR experiment.nnnRESULTSnHomo sapiens ribosomal protein L5, Homo sapiens xylosyltransferase I, and Bacteriophage T4 gene product 11 were used as test examples to verify efficacy of the proposed algorithm. In addition, the designed primers of Homo sapiens ribosomal protein L5 cDNA were applied in multiplex PCR experiments. A total of 48 forward primers and one reverse primer were designed and used to duplicate N-terminal truncated mutants of different lengths from the protein. The primers were classified into eight tube groups (i.e., test tubes) held within the same temperature range (53-57 degrees C), and the validity of the PCR products were verified using polyacrylamide gel electrophoresis (PAGE) with the functional domain correctly located. A software implementation of the proposed algorithm useful in assisting the researcher to design primers for multiplex PCR experiments was developed and available upon request.

Lossless Image Compression Based on Multiple-Tables Arithmetic Coding

This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC) method to encode a gray-level image . First, the MTAC method employs a median edge detector (MED) to reduce the entropy rate of . The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image. To promote high performance of the arithmetic encoding method, the MTAC method first classifies the data and then encodes each cluster of data using a distinct code table. The experimental results show that, in most cases, the MTAC method provides a higher efficiency in use of storage space than the lossless JPEG2000 does.

An efficient data structure for storing similar binary images

Based on the overlapping concept, this paper defines an efficient data structure, called the common-component binary tree (CCBT), to hold the linear quadtrees corresponding to a set of similar binary images. This structure removes the redundant information among the linear quadtrees. The processing time required by the operations on the CCBT structure is only proportional to the number of involved nodes in a specified linear quadtree. Additionally, there is a high storage space reduction for storing the similar images based on the CCBT structure.

Image retrieval based on tolerable difference of direction

This paper proposes two polynomial algorithms for similar image retrieval on the basis of tolerable difference of direction. A user can freely determine the tolerable difference of direction to meet his requirements. This paper then proposes an algorithm to recognize rotational invariance images. In the modified algorithm, it is unnecessary for a user to specify where the rotation center is. Experimental results show that the rotational angle estimated by this algorithm is close to the actual rotational angle if the required parameter value is well determined.