A New Descriptor for Image Retrieval Using Contourlet Cooccurrence (original) (raw)
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A Novel Content Image Retrieval Method Based on Contourlet
2008
Content-based image retrieval is an active and fast advancing research area since the 1990s as a result of advances in the Internet and new digital image sensor technologies. However, many challenging research problems continue to attract researchers from multiple disciplines. Content-based image retrieval uses the visual contents of an image as features to represent and index the image to be searched from large scale image databases. The quality of the selected features relies mainly on the degree of the invariance property that is ensured under acceptable manipulations. This paper proposes an efficient method for compactly representing color and texture features and combining them for image retrieval. The performance of retrieval based on these compact descriptors obtained by the proposed techniques is analyzed and tested on wang database images yielding satisfactory accuracy rates. A comparative study demonstrated that the developed feature extraction scheme outperformed the other schemes being compared with.
Image Retrieval using Contourlet Transform
International Journal of Computer Applications, 2011
The image retrieval problem has recently become more important and necessary because of the rapid growth of multimedia databases and digital libraries. Different search engines use different features to retrieve images from the database. In this paper, the Contourlet Transform is developed to retrieve similar images from the image database. By combining the Laplacian pyramid and the Directional Filter Bank (DFB), a new image representation is obtained. The direction subbands coefficients are used to form a feature vector for classification. The performance of the Contourlet Transform is evaluated using standard bench marks such as Precision and Recall. An experiment shows that the Contourlet Transform (CT) features provide the best results in Image Retrieval.
2010 2nd IEEE International Conference on Information Management and Engineering, 2010
Over the past few years, Content-based image retrieval (CBIR) has been an active research area. A rapid proliferation has been witnessed in the fields of both theoretical research and development of the CBIR system. The most commonly used transformation techniques in CBIR include wavelet and Fourier transformations; in spite of their widespread utilization, they have not been very effective in representing the image regions that are separated by smooth contours. An effective alternative, Contourlet Transformation performs well in representing the time-frequency localization of the images. In this paper, we propose a CBIR system for effective retrieval of images from a database for a given query image. The proposed CBIR system utilizes CT to extract the content of the image in terms of directional contours, horizontal and vertical edges of the image. In addition, the system extracts texture, color and spatial features from the images. In image retrieval, the system measures the similarity between the features of the query image and the images in the database using Squared Euclidean distance. Eventually, the images similar to the query image are effectively retrieved, chiefly based on the contourlet features.
Conventional content-based image retrieval schemes may suffer from practical applications. Image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors the interactive retrieval of such image classes is then very difficult. To address this challenge, we propose the support vector machine (SVM) algorithm with contourlet transform coefficients distribution. SVM is used to find out the optimal result and to evaluate the generalization ability under the limited training samples. It gives faster result as compared to other. An SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Features of the face image are extracted in the spectrum domain using contourlet Transform and this transform addresses the problem of representing the images with smooth contours in different directions by providing two additional properties which are directionality and anisotropy. This method will overcome the introduction of the noisy examples by the users. In the proposed technique, multiple feature distances are combined to obtain image similarity. The extensive experiments are performed on two different image data bases to validate the superiority of the proposed method.
Texture image retrieval using contourlet transform
2009 International Symposium on Signals, Circuits and Systems, 2009
Content based image retrieval is a challenging issue in management of existing large digital image libraries and databases. The accuracy of image retrieval methods is subject to effective extraction of image features such as color, texture, and shape. In this paper we propose a new image retrieval method using contourlet transform coefficients. We use the properties of contourlet coefficients to assign the normal distribution function to the distribution of coefficients in each sub-band. The assigned normal distribution functions are used to extract the texture feature vector at the next stage. Simulation results indicate that the proposed method outperforms other conventional texture image retrieval methods such as, Gabor filter and wavelet transform. Moreover, this method shows a noticeable higher performance compared to another contourlet based method.
AMALGAMATION OF CONTOUR, TEXTURE, COLOR, EDGE, AND SPATIAL FEATURES FOR EFFICACIOUS IMAGE RETRIEVAL
From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method.
Co-occurrence matrix Texture Feature based Image Retrieval Algorithms
2014
169 www.erpublication.org Abstract— Image Retrieval is the process of retrieving the most closely matched images automatically by extracting the basic features such as edge, shape, color and textures from the query image. The proposed image retrieval system is used texture feature by using grey – level co-occurrence matrix (GLCM) and Color Co – occurrence matrix (CCM). The GLCM and CCM separately combined with a color feature with the use of quantization of HSV color space. The multi-feature extraction is achieved through the Euclidean distance classifier. The proposed system performance is also measured by conducting experiments in different ways.
Investigation of Feature Extraction Methods for Image Retrieval Application
International Journal of Innovative Research in Applied Sciences and Engineering
Content-based image retrieval is a technique used for retrieval of desired images via their colour, texture, and shape features. Features play a major role in an image. The major challenge in image retrieval lies in extracting the optimal features from an image. Feature extraction is a process of selecting optimal low level feature subsets. It t ransforms the input image into a set of features that describes the image with sufficient accuracy. In this paper, three specialized features i.e. colour moments, Region properties and Grey Level Co-Occurrence Matrix (GLCM) are extracted. This Image retrieval system using the hybrid features are tested using Corel image datasets consisting of 1000 images from 10 semantic categories. The efficiency of the system is evaluated in terms of precision, recall and error rate. From the experimental results, we can conclude that these hybrid features have improved the precision of the retrieval system when compared with other state-of-the-art methods.
Optimized Content based Image Retrieval System based on Multiple Feature Fusion Algorithm
research.ijcaonline.org
Recent years have envisaged a sudden increase in the use of multimedia content like images and videos. This increase has created the problem of locating desired digital content from a very large multimedia database. This paper presents an optimized Content Based Image Retrieval (CBIR) system that uses multiple feature fusion and matching to retrieve images from a image database. Three features, namely, color, texture and shape are used. A modified color histogram is used to extract color features, the standard DWT method was combined with Rotated Wavelet Filter (RWF) features and dual tree complex wavelet transform (DT-CWT) are combined to select texture features and active contour model is used to select the shape features. K-means and SOM algorithms are used for clustering and dimensional reduction. The similarity measure used combines spatial distance, direction distance and Euclidean distance during matching process. Experimental results prove that the proposed CBIR system is an improved version in terms of precision, recall and speed of image retrieval.