A Lossless Hybrid Shape-Adaptive Image Coder

Lossless image compression continues to be the focus of the medical picture archiving system designers because of the possibility of reducing the bandwidth required to transmit medical images. The lossless Differential Pulse Code Modulation (DPCM) and Hierarchical Interpolation (HINT) have been suggested as solutions to this problem. However, there are limitations due to the inability of these schemes to adapt to local image statistics. Efforts to alleviate this problem can be seen in various adaptive schemes found in the literature. This paper introduces a new adaptive DPCM (ADPCM) scheme based on the shape of the region of support (ROS) of the predictor. The shape information of the local region is obtained through a universal Vector Quantization (VQ) scheme. The proposed lossless encoding scheme switches predictor type depending on the local shape. Simulation results show that improvements of about 0.4 bits/ pixel over basic DPCM and 0.2 bits/pixel over HINT can be obtained. Comparison with lossless JPEG indicates that the proposed scheme can cope more easily with the changes in local image statistics. The computation required is moderate, since a universal VQ is used in encoding the shape information

Scalable Multiresolution Image Segmentation and Its Application in Video Object Extraction Algorithm

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modelling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it suitable for the scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multiresolution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where downsampling distortions are more visible. Application of the spatial segmentation in video segmentation, compared to traditional image/video object extraction algorithms, produces more visually pleasing shape masks at different resolutions which is applicable for object-based video wavelet coding. Moreover it allows for larger motion, better noise tolerance and less computational complexity. In addition to spatial scalability, the proposed algorithm outperforms the standard image/video segmentation algorithms, in both objective and subjective tests

Automatic Image Annotation for Semantic Image Retrieval

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images

Image analysis using line segments extraction by chain code differentiation

This paper proposes a new fast method for line segment extraction from edge maps. It has a parallel nature and can be used on parallel machines easily. The method uses the chain codes in the edge map, namely macrochains, for line segment detection. In the first phase, it breaks the macrochains into several microchains by employing the extreme points of the first derivative of shifted-smoothed chain code function. Straight-line segments approximate the resulting microchains. In the second phase, the line segments are grouped together based on their proximity (collinearity and nearness) to make longer segments. The final set could be tailored for any minimum segment length and minimum error desired

Arabic/Persian cursive signature recognition and verification using line segment distribution

This work proposes a fast method for line segment extraction based on chain code differentiation. It is applied to cursive signature recognition of Arabic/Persian. The evaluation method is introduced to obtain a quantitative value for the recognition rate. The comparative results show the existing differences among the methods in recognition, building time and searching time criteria. The two methods used for comparison are invariant moments and CBLSE method

Sketch-based image retrieval using angular partitioning

This paper presents a novel approach for sketch-based image retrieval based on low-level features. The approach enables measuring the similarity between a full color image and a simple black and white sketched query and needs no cost intensive image segmentation. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Abstract images are obtained using strong edges of the model image and thinned outline of the sketched image. Angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using Fourier transform. The extracted features are scale and rotation invariant and robust against translation. A collection of paintings and sketches (ART BANK) is used for testing the proposed method. The results are compared with three other well-known approaches within the literature. Experimental results show significant improvement in the recall ratio using the new features

Scalable multiresolution color image segmentation with smoothness constraint

This paper presents a multiresolution image segmentation method based on the discrete wavelet transform and Markov random field (MRF) modeling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it applicable for scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multire solution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Examining the corresponding pixels at different resolutions simultaneously enables the algorithm to directly segment the images in the YUV or similar color spaces where luminance is in full resolution and chrominance components are at half resolution. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where downsampling distortions are more visible. In addition to spatial scalability, the proposed algorithm outperforms the standard single and multire solution segmentation algorithms, in both objective and subjective tests

Image database retrieval using sketched queries

This paper presents a novel approach for sketch-based image retrieval based on low-level features. It enables the measuring of the similarity among full color multi-component images within a database (models) and simple black and white user sketched queries. It needs no cost intensive image segmentation. Strong edges of the model image and morphologically thinned version of the query image are used for image abstraction. Angular-radial decomposition of pixels in the abstract images is used to extract new compact and affine invariant features. Comparative results, employing an art database (ArT BANK), show significant improvement in average normalized modified retrieval rank (ANMRR) using the proposed features

Signature-based document retrieval

This paper presents a new approach for document image decomposition and retrieval based on connected component analysis and geometric properties of the labeled regions. The database contains document images with Arabic/Persian text combined with English text, headlines, ruling lines, trademark and signature. In particular, Arabic/Persian signature extraction is investigated using special characteristics of the signature that is fairly different from English signatures. A set of efficient, invariant and compact features is extracted for validation purposes using angular-radial partitioning of the signature region. Experimental results show the robustness of the proposed method

A multi-class image classification system using salient features and support vector machines

This paper addresses the problem of automatic image annotation for semantic retrieval of images. We propose an image classification system that is capable of recognizing several image categories. The system is based on the support vector machine and a set of image features that includes MPEG-7 visual descriptors and a custom feature. The system is evaluated on a large dataset consisting of 14400 images in four categories - landscape, cityscape, vehicle and portrait. We find that the proposed edge direction histogram and the MPEG-7 edge histogram perform better than other features in this application. Experiment results indicate that the pair- wise SVM approach performs better than the one-versus-all SVM approach. The pair-wise method with confidence score voting has better classification rates compared to the pair-wise method with majority voting