Content-based Image Retrieval using Color and Geometry

The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. With the development of Multimedia data types and heavy increase in available bandwidth, there’s a huge demand of Image Retrieval system Content based image retrieval system uses color and geometry means to store, retrieve, sort and print any combinations of the images. The retrieval of images is, for the majority of search engines, available for collecting data from the image, this can be an image file name, html tags and surrounding text. This left the actual image more or less ignored. CBIR uses methods that analyze the actual bits and pieces i.e. color, shape, texture and spatial layout. There have been different approaches such as feature extraction, indexing and retrieval process. One approach is to make an attempt to classify the image into a more textual described context. With the image classified, it can be retrieved using more traditional and better retrieval methods. Our system Content Based Image Retrieval which is based on color and geometry, the system exactly does feature extraction in first step by using color, texture and shape (geometry) on images which gives there features which can be used to classify the image into different groups using distance formulas. Also the system gives relevant images as well as irrelevant images. The project thus going to work on relevance feedback of user which helps to improve the overall results

Colour Texture analysis

This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a micro-level using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection

Ridgelet-based signature for natural image classification

This paper presents an approach to grouping natural scenes into (semantically) meaningful categories. The proposed approach exploits the statistics of natural scenes to define relevant image categories. A ridgelet-based signature is used to represent images. This signature is used by a support vector classifier that is well designed to support high dimensional features, resulting in an effective recognition system. As an illustration of the potential of the approach several experiments of binary classifications (e.g. city/landscape or indoor/outdoor) are conducted on databases of natural scenes

Contour Based 3D Biological Image Reconstruction and Partial Retrieval

Image segmentation is one of the most difficult tasks in image processing. Segmentation algorithms are generally based on searching a region where pixels share similar gray level intensity and satisfy a set of defined criteria. However, the segmented region cannot be used directly for partial image retrieval. In this dissertation, a Contour Based Image Structure (CBIS) model is introduced. In this model, images are divided into several objects defined by their bounding contours. The bounding contour structure allows individual object extraction, and partial object matching and retrieval from a standard CBIS image structure. The CBIS model allows the representation of 3D objects by their bounding contours which is suitable for parallel implementation particularly when extracting contour features and matching them for 3D images require heavy computations. This computational burden becomes worse for images with high resolution and large contour density. In this essence we designed two parallel algorithms; Contour Parallelization Algorithm (CPA) and Partial Retrieval Parallelization Algorithm (PRPA). Both algorithms have considerably improved the performance of CBIS for both contour shape matching as well as partial image retrieval. To improve the effectiveness of CBIS in segmenting images with inhomogeneous backgrounds we used the phase congruency invariant features of Fourier transform components to highlight boundaries of objects prior to extracting their contours. The contour matching process has also been improved by constructing a fuzzy contour matching system that allows unbiased matching decisions. Further improvements have been achieved through the use of a contour tailored Fourier descriptor to make translation and rotation invariance. It is proved to be suitable for general contour shape matching where translation, rotation, and scaling invariance are required. For those images which are hard to be classified by object contours such as bacterial images, we define a multi-level cosine transform to extract their texture features for image classification. The low frequency Discrete Cosine Transform coefficients and Zenike moments derived from images are trained by Support Vector Machine (SVM) to generate multiple classifiers