Optimized Content based Image Retrieval System based on Multiple Feature Fusion Algorithm (original) (raw)

Enhanced Content Based Image Retrieval Using Multiple Feature Fusion Algorithms

Recentl y the usage of multimedia contents like images and videos has increased. This usage has created the problem of locating from a very large database. This paper presents Content Based Image Retrieval (CBIR) system that uses multiple feature fusion to retrieve images. The features like color, shape and texture are used. The color histogram is used to extract color feature and active contour model is used for shape extraction. K-means and SOM algorithms are used for clustering and dimensional reduction. The experimental results show that the proposed CBIR system is better in terms of precision, recall and speed of image retrieval.

Efficient Content based Image Retrieval (CBIR) Using Multi-feature Fusion Method

International Journal of Printing, Packaging & Allied Sciences, 2016

Content based image retrieval (CBIR) applications grand great amenity which guidance people find what they prefer from the massive amount of images. The images are retrieved from huge image data base availing the features comprising shape, color, and texture. Color is the absolute eminent one among vision features of image. Color Coherence Vector (CCV) is related to the color histogram method and it still favor some spatial feature, and is justified to be more competent. An improved CCV method is presented in this paper with higher spatial information and without abundant added computing work. Multi resolution Gabor filters and Gray level co-occurrence matrix are availed for extracting texture features. Fast Fourier Descriptor method is needed for extracting shape features. Then combining these three features, fused features are achieved. Finally, the fused features are utilized in image retrieval and best retrieval performance is shown by this multi-feature fusion retrieval method.

CONTENT BASED IMAGE RETRIEVAL ON COLOR, TEXTURE AND SHAPE FEATURES USING DWT AND MODIFIED K-MEANS

Here we proposed algorithms for CBIR system on the basis of texture, shape,and color based feature extraction and matching of color and texture.We used the Discrete Wavelet transform for decomposition of images and clusters calculations using modified K-Means clustering.We extract texture,shape, and color and finaly measure similarity between query image and database image and reduced semnatic gap between local features and global features. Integrated approach retrive more accurate image, reduce semantic gap between local and high level features.The time taken by Modified K-Means is less as comparison to other algorithms.This is more optimized method for small as well as large database.

An Optimized Feature Extraction Technique for Content Based Image Retrieval

2013

Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 , 45 , 90 , 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is co...

A Proposal of an Efficient Feature Extracting Method for Content-Based Image Retrieval

Engineering and Technology Journal

Searching a required image from the World Wide Web (WWW) is very difficult because the WWW contains a huge number of images. To solve such a problem, an efficient system is needed to retrieve images that are required by the user. The content-based image retrieval (CBIR) system has been used to solve this problem. In this paper, a new combination of three techniques is used for visual features extracting. Color histogram was used to extract color feature from the image. Multi wavelet transform was chosen to represent the information of the texture and the edge histogram was used to represent the shape feature. Object scaling and translation in an image can be got robustly by the combination of these techniques. Furthermore, to speed up retrieval and similarity computation of the proposed system, the data set images are clustered using k-mean clustering algorithm according to the weighted feature vectors. The system evaluation experimentally carried out on800Wang color image dataset, and showed that proposed system performed significantly better and faster than other existing systems by using the proposed features.

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.

Content- based Image Retrieval Approach using Three Features Color, Texture and Shape

International Journal of Computer Applications, 2014

Content Based Image Retrieval is a technique of automatic indexing and retrieving of images from a large data base. Visual features such as color, texture and shape are extracted to differentiate images in Content Based Image Retrieval (CBIR). Each of the features can be represented using one or more feature descriptors. These features descriptors combined with form feature vectors and are used together. During the retrieval, features and descriptors of the query are compared with the available images in the database. The images are then retrieved from database on the basis of distance of their feature vectors. At present, information of the maximum two features have been utilized for comparing the image and these methods provides the less accurate result. In our proposed work, more than two features i.e. three features are used for comparison and retrieval of image from the database. These three features are color, shape & texture features for image retrieval and provide more accurate results. These features are combined to fulfil the aspect of retrieval in image. The proposed work uses HSI color information especially Hue value, Fuzzy C-Mean algorithm for shape representation and co-occurrence matrix is used for texture feature extraction.

An Enhanced Method for Content Based Image Retrieval

2015

The content based image retrieval (CBIR) is the wellliked and heart favorite area of research in the field of digital image processing. The key goal of content based image retrieval (CBIR) is to excerpt the visual content of an image directly, like color, texture, or shape. There are several applications of the CBIR technique such as forensic laboratories, crime detection, image searching etc. For the purpose of feature extraction of well-matched images from the database, a universal CBIR system utilizes texture, color and shape based techniques. In this presented work, we have offered an efficient approach for the content based image retrieval, where images are decomposed using the wavelet transform, it means that the image features are converted in the matrix form and a color feature data set is prepared. In order to improve search results we have used k-means algorithm. It is shown by experimental results that, the efficiency of the proposed method is improved in contrast with the existing method.

Fusion-Based Method for Content Based Image Retrieval

2018

The content-based image retrieval (CBIR) has gained the substantial attention during the past few years, with many potential practical applications,. The CBIR system utilizes the visual features of an image such as color, texture and shape to retrieve the related images. The system extracts the features of a query image, searches the database for images with similar features, and exhibits relevant images to the user in order of similarity to the query. The proposed system uses the color features and the texture features to retrieve the related images. Both the color features and the texture features are combined to retrieve more accurate results. Hence a fusion based model is developed which uses hue saturation and value (HSV) to extract the color features and Gabor wavelet to extract the texture features. The experimental results show that by using both color and texture feature for retrieval can significantly improve the performance of the CBIR systems. IndexTerms – CBIR, Color, T...