Papers by Keyword: CBIR

Abstract: Content-based image retrieval (CBIR) system can be used to effectively and precisely retrieve the desired images from a large image database, and the development has become an important research issue.Statistical methods like, gray level co-occurrence matrix (GLCM) and the autocorrelation function are used to extract texture feature. Region-based methods utilize information from both boundaries and interior regions of the shape. Shape features like perimeter, area, centroid, circularity, solidity based on region can be extracted in the feature space. Similar images can be retrieved using minimum distance classifiers with and without clustering algorithm .Time complexity and the retrieval efficiency has been analyed and compared on both the methods. The experiments have been conducted on MPEG-7 dataset.

Abstract: The target of this paper is to introduce the improvement of the technique of image retrieval. At first, it comes up with the concept of image retrieval and shows the importance of this technique. As the techniques of multimedia and Internet are developing rapidly, the resources of images that users obtain are also extended. And then this paper gives the problem about the image retrieval, namely the information of images are disordering. As the result, it is significant to do the effective organization, management and retrieval based on the increasingly extensive image information storage. After that, this paper presents the concept of TBIR and CBIR and gives the definitions of them. It proposes an issue that CBIR is the improvement of TBIR. Based on CBIR, there are also some disadvantages that need to be improved. In terms of the main point of CBIR, the paper raises that the annotation is one of the most difficult techniques that need to be promoted. Then it describes some algorithms about the technique of automatic image annotation. After these algorithms, the paper shows the challenges and developing direction of the technique of image retrieval. At last, it presented the conclusion to emphasize the main points of this paper.

Abstract: Color is one of the important characteristics in visual perception. But color histograms used commonly loses spatial distribution information while obtains the statistical feature of image color. This paper provides an algorithm, the region partitioning algorithm based on concentric circles, that synthesizes the color, texture and space information to extract features of image. It evidently improves the precision of retrieval and achieve better performance.

Abstract: An efficient image indexing method based on class specific hyper graph is proposed. The presented indexing method works efficiently and the relevance of the original image data is enhanced. Because of that an ordered image database benefits the efficient searching. The relevance of images depends on the similarity between different images. According to clustering theory, we can take any sample image in the database as one clustering center, and then the siblings of the center and their siblings are consistently searched, which is known as similarity spread. After that, the disordered image database is sorted out and the searching result is not tedious any more. The proposed method has been tested by an open arts press image database, which shows that our method can obviously improve the indexing speed. Moreover, the indexing results make the whole image searching system capable of association.

Abstract: It is known that wavelet transform provides very useful feature values in analyzing various types of images. This paper presents a novel approach for content-based textile image retrieval which uses composite feature vectors of low-level color feature from spatial domain and second-order statistic features from wavelet-transformed sub-band coefficients. Even though color histogram itself is efficient and most used signature for CBIR, it is unable to carry local spatial information of pixel and generate inaccurate retrieval results especially in large image data set. In this paper, we extract texture features such as contrast, homogeneity, ASM(angular-second momentum) and entropy from decomposed sub-band images by wavelet transform and utilize these multiple feature vector to retrieve textile images combining with color histogram. From the experimental results it is proven that the proposed approach is efficiently retrieve the desired images from a large set of textile image database.

Abstract: In HV segmentationthe IFS records the compression transform parameters which reflect thecross-scale redundancy features between different regions of a natural imageand have better adaptability. This paper proposed a new image fast fractalencoding method based on HV Segmentation, and brings forward the Similarityjudgment formula. This method significantly improved the retrieval effect withdecoding quality assurance. The experimentation shows the accurate-completeretrieval rating of this method is obviously superior to the color histogrammethod.

Abstract: In existing image search systems, image queries can be used to find similar images through content-based image retrieval (CBIR). In order to obtain more related images, users often need to provide descriptions of the image as the keywords for search engine to extract more relevant information. But it is difficult to find appropriate keywords and text description from the image content. Searching for relevant information from search engines takes a lot of time. In this paper, we propose a CBIR system which effectively finds similar images by comparing image contents and the image annotation embedded in the image. First, we use discrete wavelet transform and two-dimensional code to embed the relevant text information or tags into the image. Then, we extract color ratios and Scale-Invariant Feature Transform (SIFT) descriptors as the image features for similarity matching. The experimental results showed that our proposed approach can accurately find similar images, and extract image-related textual information to provide useful tags for users.

Abstract: In this paper an algorithm is proposed to retrieve images based on contour moment invariants of image and relevance feedback. Firstly, the contour of each query image is extracted and its contour moment invariant is computed. Then according to Euclid Distance between the query image and each image in the image database, the most similar images to the query image can be found. Finally, the relevance feedback algorithm based on support vector machine (SVM) is applied to improve retrieval precision. Experimental results show that the algorithm is more accurate and efficient to retrieve images with monotonous background and clear object and meet the invariance on shift, rotation and scale transform of objects.

Abstract: We propose a practical cartoon image retrieval scheme to retrieve cartoon images efficiently. The proposed scheme transfers each cartoon image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the cartoon images, namely color sequences comparison. We succeed in transferring the cartoon image retrieval problem to sequences comparison. Thus the computational complexity is decreased obviously. Our system keeps both advantages of the content based cartoon image retrieval system (similarity-based retrieval) and a text based cartoon image retrieval system (very rapid and mature).

Abstract: Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. There are various features which can be extracted from the image which gives different performance in retrieving the image.al systems. In this paper we have tried to compare the effect of using different features on the same data base to implement CBIR system. We have tried to analyse the retrieval performance for each feature. We have compared different features as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.