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Presently, corporations and individuals have large image databases due to the explosion of multimedia and storage devices available. Furthermore, the accessibility to high speed internet has escalated the level of multimedia exchanged by users across cyberspace every second. Accordingly, it has increased the demand for searching among large databases of images. Conventionally, text-based image retrieval...
Under the environment of big data, retrieval becomes a crucial technology and image retrieval is paid more attention and widely used. The paper proposes a second-order retrieval algorithm, of which can be used to retrieval the similar images. Firstly, extracting image sift features. Then, build frequency table of characteristic words by k-means clustering and bag of word algorithm. Finally, based...
The retrieval in multimedia database is research focus in computer vision. In this paper, we propose a novel boosting image retrieval framework. In our work, a new method is proposed to extract salient objects in the images in order to eliminate the interference of the background. Then an effective framework for image retrieval is introduced with weak classifier. To evaluate the validity of the proposed...
RGIRS (Remote Geo-system Image Retrieval System) is a system of retrieving similar image using image features like color feature, texture feature and shape feature. Content based image retrieval system extracts features relevant to query image using feature extraction method. Many RGIRS systems are proposed to retrieve accurate similar image but the problem is no method provides accurate results....
The determination of Region-of-Interest can be used as a means of improving the performance of image retrieval, when used in image annotation as a step in the indexing of images collection. It also has the potential to support efficient video compression for real-time applications. However, existing Region-of-Interest detection methods are mostly unsuitable for managing large number of images and...
In this paper we propose an effective method of aerial image classification, which combines three types of features: color-based, statistical and fractal information. Two distinct phases were necessary for the CBIR system, which includes the classification algorithm: the learning phase and the classification phase. In the learning phase 5 different and efficient features were selected: entropy, contrast,...
SIFT features have been found to be effective in describing image textures. Because SIFT features have some great characteristics, such as translation invariance, zooming in and out invariance, spin invariance and affine invariance, etc, so the image retrieval precision is satisfactory usually. However, in Content Based Image Retrieval (CBIR), there are so many SIFT feature points extracted from an...
Content-Based Image Retrieval (CBIR) refers to techniques that retrieve images based on their content, as opposed to based on metadata. A CBIR system performs indexing and retrieval tasks using features like color, texture and shape computed from images as opposed to using the whole images. In the medical field, content based image retrieval is used to aid radiologist to retrieve of images with similar...
During the years image classification gained important significance in practice, especially in the fields of digital radiology, remote sensing, image retrieval, etc. Typical algorithm for image classification contains descriptor extraction phase, learning phase and testing phase. Testing phase calculates accuracy of the classifier based on predetermined set of labelled images. This paper analyse performance...
The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous...
High dimensional visual features and multimodal based search systems are popular due to their visual retrieval accuracy. But high dimensionality and multimodality negatively affect efficiency and computational complexity. This paper brings the performance advantages of low dimensional visual feature called Compacted Dither Pattern Code (CDPC). CDPC is accompanied with texel properties, spatial colour...
Due to the prevalence of digital cameras, it is easy to retrieve digital images from the Internet. With the rapid development of digital image processing, databases, and Internet technologies, how to efficiently manage a large amount of digital images is very important. In this paper, we proposed a novel approach for automatic image annotation. We extract color, texture, and shape features from a...
This paper examines the Kernel Principal Component Analysis (KPCA) feature detection and classification for underwater images. In Underwater images the numbers of distortion occurred are blurring of image, illumination of light and rotation of angle, noise etc. Features are normally extracted by the method called SIFT (Scale Invariant Feature Transform for underwater images). It is used for extracting...
We address the problem of retrieving the silhouettes of objects from a database of shapes with a translation and rotation invariant feature extractor. We retrieve silhouettes by using a “soft” classification based on the Euclidean distance. Experiments show significant gains in retrieval accuracy over the existing literature. This work extends the use of our previously employed feature extractor and...
Pornographic image recognition and filtering are of great significance for web security and content monitoring. In this paper, an adult image recognition method based on support vector machine (SVM) and erotic category is proposed. Global color and texture features and local SIFT feature are extracted to train multiple SVM classifiers for different erotic classes. Face detection is used to filter...
The traditional CBIR (Content based image retrieval) global descriptor can't extract local features effectively, while the SIFT (Scale Invariant Feature Transform) algorithm only focus on local object features matching, and can not satisfy the need of fuzzy CBIR queries based on visual feeling. In view of this contradiction, an approach is proposed based on image salient region, in which the SIFT...
Digital image processing is a rapidly growing area of computer science since it was introduced and developed in the 1960's. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification. Colour of the flower plays very important role in image classification since it gives additional information in terms of segmentation and recognition. On...
The widespread utilization of digital visual media has motivated many research efforts towards efficient search and retrieval from large photo collections. Traditionally, SIFT feature-based methods have been widely used for matching photos taken at particular locations or places of interest. These methods are very time-consuming due to the complexity of the features and the large number of images...
Content-based image retrieval (CBIR) is a difficult area of research in multimedia systems. The research has proved extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one objects and to segment the image in line with object features to extract meaningful...
Organizing images into meaningful (semantically) categories using low-level visual features is a challenging and important problem in content-based image retrieval. Clustering algorithms make it possible to represent visual features of images with finite symbols. However, there are two problems in most current image clustering algorithms. One is without considering the choice of the initial cluster...
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