CBIR: Effective Utilization of Image Database (original) (raw)
Related papers
Integrated color, texture and shape information for content-based image retrieval
Pattern Analysis and Applications, 2007
Feature extraction and the use of the features as query terms are crucial problems in content-based image retrieval (CBIR) systems. The main focus in this paper is on integrated color, texture and shape extraction methods for CBIR. We have developed original CBIR methodology that uses Gabor filtration for determining the number of regions of interest (ROIs), in which fast and effective feature extraction is performed. In the ROIs extracted, texture features based on thresholded Gabor features, color features based on histograms, color moments in YUV space, and shape features based on Zernike moments are then calculated. The features presented proved to be efficient in determining similarity between images. Our system was tested on postage stamp images and Corel photo libraries and can be used in CBIR applications such as postal services.
Content Based Image Retrieval Using Color, Texture and Shape Features
15th International Conference on Advanced Computing and Communications (ADCOM 2007), 2007
In content-based image retrieval (CBIR), content of an image can be expressed in terms of different features such as color, texture, and shape. In this paper, the color information of an image is represented by the Global HSV Color Histogram. The texture information is described by the variance of each wavelet subband in compressed domain with the emphasis that subbands are not buffered to maintain memory efficiency. In addition, the Gabor Filter has been used also to extract the texture features. The Edge Detection is represented here by the Edge Direction Histogram. The shape information is represented by the Zernike Moment Descriptor (ZMD). The image retrieval is indexed by both individual features and combined image features. A Java-based retrieval framework has been developed to conduct the online retrieval framework to compare the retrieval performance and speed. Experiments are performed with 52 texture patterns and different similarity measures over the VisTex database. The experimental results show that the image retrieval using combined features outperforms retrieval using individual features.
Image retrievalis a technique to retrieve images by utilizing the features of the image like color, shape and texture. Almost all of the current image retrieval or CBIR (content-based image retrieval) system allow for querying-by-image, a technique wherein animage (or a single feature of an image) is selected by the user as the query. The system extracts the features of the query image, searches for images with similar features in the database, and return relevant result in the form of image to the user in order of their similarity to query. Image retrieval is very useful in many areas like art collection, face finding, crime prevention, photograph archives etc. There are several techniques or algorithms that are used for feature extraction in content based image retrieval. This paper create a review of techniques or number of those methods that are used for feature extraction in content based image retrieval that are Color histogram, Color moments, Gabor filter, Wavelet Transform, Zernika Moment(ZM) ,Chain code etc.
Content-Based Image retrieval using Color, Texture, Edge Detection, and Shape: A Comparative Study
2006
In content-based image retrieval (CBIR), content of an image can be expressed in terms of different features such as color, texture, and shape. In this paper, the color information of an image is represented by the Global HSV Color Histogram. The texture information is described by the variance of each wavelet subband in compressed domain with the emphasis that subbands are not buffered to maintain memory efficiency. In addition, the Gabor Filter has been used also to extract the texture features. The Edge Detection is represented here by the Edge Direction Histogram. The shape information is represented by the Zernike Moment Descriptor (ZMD). The image retrieval is indexed by both individual features and combined image features. A Java-based retrieval framework has been developed to conduct the online retrieval framework to compare the retrieval performance and speed. Experiments are performed with 52 texture patterns and different similarity measures over the VisTex database. The experimental results show that the image retrieval using combined features outperforms retrieval using individual features.
CONTENT BASED IMAGE RETRIEVAL : A REVIEW
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. The paper discusses effective indexing and fast searching of images based on visual features.
A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval
International Journal of Advanced Science and Technology, 2015
Image Retrieval is the basic requirement of today's life in present scenario. Because of huge amount of different types of images are added in database from different sources for retrieval of the image, different kinds of processing is required to extract the relevant features from them. In this paper, comparisons of combination texture and shape features are done with texture Gray Level Co-occurrence Matrix and Hu-moments and the combination of tamura texture and shape invariant Hu-moments. For the performance evaluation of the system we use most commonly used methods namely precision and recall.
Content Based Image Retrieval using Color, Shape and Texture Extraction Techniques
Abstract— Images contain information in a very dense and complex form, which a human eye only after years of training can extract and understand. In Content-Based Image Retrieval (CBIR), visual features such as color, shape and texture are extracted to characterize images. How to extract ideal features that can reflectthe intrinsic content of the images as complete as possible is still a challenging problem. This paper mainly focuses on representing the image in terms of low level features extracted from the image. These low level features primarily constitute colour, shape and texture features. By processing the extracted features instead of the entire image, reduces the memory requirements as well as the computational time required to process the image. Each of the features is represented using one or more feature descriptors. During the retrieval, features and descriptors of the query are compared to those of the images in the database in order to rank each indexed image according to its distance to the query. The distances of the various database images to the query image are sorted in order to calculate the similarity between them. The patterns from the candidates are retrieved from database by comparing the distance of their feature vectors. The CBIR technology has been used in several day-today applications such as fingerprint identification, biodiversity information systems, digital libraries, crime prevention, medicine, historical research, biomedical field etc. In this paper the different colour, shape and texture feature extraction techniques have been studied and implemented in order to obtain the desired results. The results also prove that only colour, shape or texture features are insufficient to describe the entire image, and thus a combination of two or more feature extraction techniques is required to obtain best results.
Color and Texture Features for Image Indexing and Retrieval
2009 IEEE International Advance Computing Conference, 2009
computed and stored to construct indexing feature The novel approach combines color and texture vectors. The Gabor wavelets are a group of features for content based image retrieval (CBIR). wavelets, with each wavelet capturing energy at a The color and texture features are obtained by specific frequency and a specific direction. computing the mean and standard deviation on Expanding a signal using this basis provides a each color band of image and sub-band of different localized frequency description, therefore wavelets. The standard Wavelet and Gabor capturing local features/energy of the signal. wavelet transforms are used for decomposing the Texture features can then be extracted from this image into sub-bands. The retrieval results group of energy distributions. The scale obtained by applying color histogram (CH) + (frequency) and orientation tunable property of Gabor wavelet transform(GWT) to a 1000 image Gabor filter makes it especially useful for database demonstrated significant improvement in constructing indexing feature vectors. precision and recall, compared to the color
Image Retrieval based on Integration between Color and Geometric Moment Features
2012
Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work d eals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retriev...