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Image annotation methods construct a Tag distance matrix, which entries show the relevancy of tags for each test image. More accuracy in calculating this matrix provides better annotation results. The aim of our two methods is to improve the accuracy of the Tag distance matrix using the class information already available in most datasets. If the class information is not available, extracting important...
While much progress has been achieved in the field of content-based image retrieval (CBIR), almost all CBIR techniques operate on pixel data although virtually all images are stored in compressed form. In this invited paper, we present efficient and effective CBIR techniques that operate directly in the compressed domain and thus do not require full decompression for feature extraction. In particular,...
The use of multimedia data such as images, audio and video etc in daily life led to a huge amount of images in fairly large image databases. Nowadays, the image becomes a focal source of information because it hides a precious knowledge. In Image Mining, the aim is to discover this knowledge and gives the relevant information or patterns that are presented. This paper is dedicated to a review on image...
Technology brings images as a communication media for humans. Image communication used today in many fields such as education, media, healthcare and in other domains. Based on image retrieval user input selection one of the most powerful technique and has been an active research direction for the couple of years. Various features are used for image retrieval. Most of the retrieval technique used image...
This paper presents an epigrammatic overview of role of knowledge in image retrieval and recent advances in image retrieval techniques. We also proposed a new image retrieval technique for content based image retrieval system which incorporates image mining, amalgam knowledge management technique and advance concept based image retrieval technique. Proposed system excavated knowledge by mining large...
In this paper, we present the visualization of image databases based on their primitive features. Our approach is to have a visual navigation tool for allowing the exploration and exploitation of large image archives. The tool is able to project the content of a given image database based on the primitive feature space and to provide interaction between the final user and the huge amount of data....
Local patterns have two problems: 1) the traditional local patterns methods only consider the frequency of each pattern, and does not consider the co-occurrence information between adjacent pixels pairs in the image; 2)the traditional methods limit on the gray texture analysis, ignoring the importance of color information. To address above problems, a novel method is proposed for color image retrieval...
Feature extraction simplifies the amount of information needed to describe the properties of an image accurately. This paper measures the performance of a CBIR system based on texture feature against combination of both color and texture feature. A Gray Level Co-occurrence Matrix is calculated for computing the texture feature of an image. Using these textual parameters similar images are extracted...
The numbers of digital images are increasing day by day and mining from large databases is becoming harder & harder. Indexing image data based on text is tiresome and error prone. If the indexing based on low-level feature of the image then it may reduce the workload and mining become faster. In this research paper we propose an indexing technique which indexes the digital images in the database...
This work introduces an image retrieval framework based on using deep convolutional neural networks (CNN) as a local feature extractor. Motivated by the great success of CNN in recognition tasks, one may be tempted to simply adopt the output of CNN as a global image representation for retrieval. This straightforward approach, however, has proved deficient, because it can be vulnerable to various image...
With the rapid development of the Internet and speedy increase of the data size, there are more and more data intensive applications which often involve hundreds of megabytes of data. It is important and necessary to obtain the retrieval results from cross-media data quickly and accurately. Large scale cross-media data retrieval based on Hadoop is proposed to speed up the retrieval in this paper....
the gray gradient co-occurrence matrix based Uyghur document image retrieval method is proposed in this paper. 15 parameters of gray gradient co-occurrence matrix such as small gradient advantage, high gradient strengths, intensity in homogeneity etc are extracted separately, and document image features are matched with the characteristic distance classifier and the Euclidean distance classifier....
Recently, two improved methods have shown their advantages in browsing Earth Observation (EO) dataset. The first method is the Bag-of-Words (BoW) feature extraction method and the second is the Normalized Compression Distance (NCD) for assessing image similarity. However, they have not been compared so far for satellite image retrieval, which motivates this paper. Two retrieval experiments have been...
Multimedia and image mining representing the image objects clearly and efficiently because image objects are hard to define. Thus, we have to break the image object into meaningful components such as color, texture, shape, etc. Querying the image objects after representing them to retrieve the discovered knowledge. In the age of Big Data where Velocity, Variety and Volume are the challenges, variety...
Objective of our paper is to discuss latest pattern recognition applications, techniques and development. Pattern recognition has been demanding field from many years. We are also discuss driving force behind its swift development, that is pattern recognition is used to give human recognition intelligence to machine which is soul of today's many modern application. It acts as wheel of many techniques...
Image data mining can be done manually by slicing and dicing the data until a pattern becomes obvious. Or, it can be done with programs that analyse the data automatically. Colour, texture and shape of an image have been primitive image descriptors in Content Based Image Retrieval (CBIR) system. Primitive features of an image used to identify and retrieve closely matched images from an image database...
The state-of-the-art image retrieval approaches usually quantize SIFT features into visual words. Researchers proposed the notion of bundled feature which simply employs MSER to bundle SIFT features into groups. In this paper, we propose a large scale partial-duplicate image retrieval scheme using invariance weight of SIFT and SROA geometric consistency based on the bundled feature. We calculate the...
A new retrieving algorithm is presented in this paper to overcome the weakness of the traditional image retrieving method, such as the huge calculation amount, low transmission rate, and the response delay. This algorithm firstly set the image binaryzation, then take the o,1 sequence mode as the image characteristics to realize the similarity matching of the images. The experimental result shows that...
This work is focused on the modeling and development of a CBIR (Content-based image retrieval) system applied to the recovery of digital medical images of a human body, denominated M-CBIR. This model is composed on two methodologies: features extraction techniques and metric data structures. When this set of techniques is applied to the search of different human body regions, it can retrieve the most...
Existing image retrieval frameworks use the low-level visual features extracted from images to learn a set of semantic categories and tend to discard the conceptual properties of these features. In this paper, we propose to use the conceptual information of the low-level image features to establish a correspondence between the image features and a set of high-level semantic categories. In doing so,...
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