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In Bag-of-Words-based image retrieval, the local feature could not describe the global information of an image. It produces many false matches and reduces the retrieval precision. To address this problem, this paper proposes a new method which is based on the global and local feature similarity. The global feature extracted by convolutional neural network is added to the local keypoints extracted...
In recent years, many approaches based on mathematical morphology have been applied in remote sensing image processing. In the paper, we proposed to use attribute profiles (APs) for textural extraction to the problem of content-based image retrieval (CBIR). In particular, four different attributes of APs with multiscale were applied to spatial information description. Then, the obtained textural images...
In recent years, the demand for directly visual searching based on images and videos becomes stronger and stronger. Most large-scale image retrieval systems are based on the Bag of Words (BoW) or its variant. For current visual search algorithms which are under the structure of BoW, there are two critical issues should be solved: the quantified visual words may reduce the discriminative power of the...
With the rapid growth of image database, content-based image retrieval (CBIR) technology with its high value in theory and application has become a hot topic in the field of image processing. A retrieval method which combines color and texture feature is proposed in this paper. According to the characteristic of the image texture, we can represent the information of texture by fusion algorithm combines...
Due to robustness feature color of object has been proved as low level unique feature. If we change viewpoint looking at a color we note change in its appearance. Color image processing requires dealing with each and every pixel of an image. In this paper spatial color distribution features are implemented. We focus on its calculation and comparison of retrieval efficiency using histogram quadratic...
This paper describes an multi-features indexing system for use in Content Based Image Retrieval. The standard CBIR approach is simple and usually use a single information such as shape, scale or color, which leads to recognition problems in some cases, to remedy this problematic, we use additional features to combine types of information. From many points of view local descriptors are relatively different,...
In this paper, we propose new image features called perceptual colour features. The features are based on twenty basic colours called emotional colours, which are used to describe the relationship between colours and emotions in psychological studies. We analyzed a colour image in L*a*b* and L*C*h colour spaces to link to emotions. Then, the perceptual colour features are derived from the histogram...
Image retrieval plays a major role in security systems to extract the images with similar features or patterns, to retrieve the relevant images in web search engines, in industries to detect crack in the manufactured parts, in architectural designs to find same texture patterns and so on. To accomplish efficiency in all the fields of image processing, the effective image retrieval mechanism is imminent...
In the community of image retrieval, single feature shows a very low discriminative power for matching, and typically the frequently-used SIFT feature only describes the local gradient distribution. Therefore, false matches occur prevalently. Besides, conventional inverted index usually returns long candidate lists for queries, with sparse subdivision of search space and limited accuracy. To tackle...
This paper presents a hybrid image retrieval system which integrates Neural Network and Genetic Algorithm together. Proposed method reduces the semantic imbalance between the machine description and the human semantics of an image by using low-level feature descriptors- HSV color histograms, color moments, and wavelet transform, which matches human perception. These descriptors when used to train...
As the database have huge amount of images and other files it is difficult to retrieve the required image. The performance of existing method is not up to the mark and accuracy is also low. The novel approach identifies contents of the image using provided tags and each tag is associated with a class. Annotation is made based on the vector values. Here whole image is considered as one segment and...
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 2D complex Dual-tree Discrete Wavelet Transform is developed. Features are extracted from 2D CDT-DWT from YCbCr image as it provides higher spatial localization and lower...
Image retrieval is the project which allows performing search among pictures from internet resource. Instead of usual keyword and tag based search, images will be retrieved depending on its contents relevance.
Content based image retrieval systems use the contents of the images to represent and access the images. Content basically refers to the image descriptors like color, texture and shape of the image. Among the different image features, edges are the important one as edges represent mainly the local intensity variations. But in the case of color images in order to obtain satisfactory results, we must...
Relevance feedback has been employed in Content Based Image Retrieval systems to bridge the semantic gap between the low level features and high level semantics of the image. This paper proposes a short term learning relevance feedback algorithm that utilizes the statistical features of the feedback images for determining the relevance of the candidate image in the next iteration and for achieving...
The computational models of visual attention, originally pro posed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition. These systems are generally evaluated against eye tracking data or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending...
Histograms of Oriented Gradients (HOG) feature has been successfully used in pedestrian detection and achieves high accuracy. This paper introduces a content retrieval algorithm based on improved HOG. The method has two steps which are adjusting the HOG structure by scanning the image with a sliding HOG window and reducing feature dimension by principle component analysis (PCA) technique. The experimental...
The precision of visual matching and the trade-off between accuracy and time efficiency have long been bottlenecks of image search systems. This work addresses the two problem simultaneously by introducing the coupled Multi-Index (cMI) structure. First, by combining SIFT and color features on the indexing-level, the discriminative power of visual words is greatly enhanced. Second, by reducing the...
In order to realize the content-based image retrieval (CBIR), some characteristics of the images should be extracted like color, texture and shape. The extremely important thing in CBIR is to search the most similar database images to match the query image, which needs to improve the precision. This paper proposes an Improving Precision Priority (IPP) algorithm integrating vital features and the query...
This paper presents a novel image feature representation method, called local texture-based color histogram (LTCH), for content-based image retrieval. The LTCH can describe the color distribution under a mask, which is defined as a micro-structure image with a near-uniform texture. The near-uniform texture is exacted by center symmetric local trinary pattern (CS-LTP) and micro-structure map. The CS-LTP...
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