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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....
An image retrieval system is a technique for browsing, searching and retrieving images from a big database of digital images. In this paper, we propose a new content-based image retrieval system that can solve the object and scene recognition problems and categorize similar images. The proposed model consists of a deep structure support vector machine with Gaussian mixture model, which is combined...
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...
Content based video retrieval and content-based image retrieval are the hot research topics in recent years. Image feature extraction has played a very important role in the retrieval process. In this paper, we use the image color features and the image fingerprint extracted by the improved perceptual hash algorithm. In order to combine these two features, we have done a lot of tests to find the optimal...
The paper proposes a mobile application for clothing coordination, which could be of great benefit for stores and people seek for fashion advices. The application matches apparel image input with, previously saved apparel images, and then provides the user with the possible matching suggestions based on the apparel outline and dominating colors. For this purpose two Region of Interest (ROI) extraction...
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...
In this paper, a novel image descriptor, called Color Binary Correlation (CBC), is proposed for image retrieval. This method defines and describes the structure elements utilizing binary patterns based on colors and edge orientation respectively, and thus integrate texture with the other two properties. Besides, its variants CBCri and CBCu2, which are presented for rotated invariance and “unform”...
This paper proposes a novel model, called Similarity Based on Visual Attention Features (SimVisual), to enhance the similarity analysis between images by considering features extracted from salient regions mapped by visual attention models. Visual attention models have demonstrated to be very useful for encoding perceptual semantic information of the image content. Thus, aggregating saliency features...
It has very important practical significance to analyze and research minority costume from the perspective of computer vision for minority culture protection and inheritance. As first exploration in minority costume image retrieval, this paper proposed a novel image feature representation method to describe the rich information of minority costume image. Firstly, the color histogram and edge orientation...
Image retrieval deals with the problem of finding relevant images to satisfy a specific user need. Many methods for content based image retrieval have been developed over the years, ranging from global to local features and, lately, to convolutional neural networks. Each of the approaches has its own benefits and drawbacks, but they also have similarities. In this paper we investigate how a method...
Content based image retrieval is a way of indexing or finding images in a database those are similar to a query image. This process uses visual contents of images and provides more effective management for automatic retrieval of images of interest than the traditional tagged based approach. In this paper, color and texture features are used as visual contents to retrieve similar images from the database...
Many partial-duplicate image retrieval systems use the whole image for features extraction, while there is only a small duplicate region between the partial-duplicate images. On the other hand, many researchers consider the SIFT (Scale-Invariant Feature Transform) feature as an important descriptor in image retrieval systems, whereas it is independent of color and just describes the local gradient...
Retrieving images from the large amount of database based on their content are called content based image retrieval. It is a basic requirement of retrieve the relevant information from huge amount of image database according to query image with better system performance. Color and shape feature of image is most widely used feature to analyze the image. In this paper, proposed method is integrating...
Video-based object recognition faces the problem of multi-view object variance, noisy conditions, and limited computational resources. In our previous work, we introduced a multi-view recognition approach with a compact global image descriptor coupled with orientation sensor data. Since our purpose is to run all computations in a handheld device, contrary to more intensive deep learning approaches,...
In this study, an application that can retrieve images similar to a given query image is developed. The application extracts multiple image descriptors related to color and texture from the images and use them to rank the images in its database according to their similarity to the query image. It displays the most similar images on screen, after which the user give feedback about the relevancy of...
In image processing research field, image retrieval is extensively used in various application. Increasing need of the image retrieval, it is quiet most exciting research field. In image retrieval system, features are the most significant process used for indexing, retrieving and classifying the images. For computer systems, automatic indexing, storing and retrieving larger image collections effectively...
With the rising popularity of mobile devices, recent years have witnessed a growing interest in document image retrieval (DIR). In conventional Bag-of-Visual-Words (BoVW) based document image retrieval method, only SIFT or SURF feature is used to locate feature points and produce a codebook, which has a low discriminative power. Though several multiple feature based BoVW methods are proposed, these...
With image databases expanding at a rapid rate, effective and efficient methods of managing and querying these collections are highly sought after. In this paper, we present image browsing as an alternative to retrieval based systems. Image browsers provide a visualisation of a complete image database together with tools for interactive and intuitive exploration of the dataset. As examples we give...
Last two decades have seen a rapid increase in the size of digital image collections. Content based image retrieval (CBIR) provides an efficient way to search and retrieve images from these large databases. In this paper, CBIR using feature level fusion of 2D complex Dual-tree Discrete Wavelet Transform and local binary pattern (LBP) is developed. Features are extracted from 2D CDT-DWT from YCbCr...
With the development of remote sensing (RS) techniques, the amount of RS images increases dramatically. It is a challenge to utilize those RS big data efficiently. Content-based Image Retrieval (CBIR) is a typical approximate similarity search problem, which needs to establish an effective index structure to reduce the time of retrieval. By analyzing the limitations of commonly-used indexing mechanisms...
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