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The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This proposed paper presents the content based image retrieval for medical applications, using texture features and shape. This method uses a texture spectrum...
In this work we propose an approach based on shape clustering for image retrieval. Firstly, shapes of objects contained into images are represented by means of Fourier descriptors. Then, a fuzzy clustering process is applied to automatically discover a set of shape prototypes representative of a number of semantic categories. The adopted fuzzy clustering algorithm is equipped with a mechanism of partial...
This paper presents a novel framework for image retrieval. In image feature extraction stage, we propose the gradient histogram Markov stationary features to represent the input image which is capable of characterizing the spatial co-occurrence of gradient histogram patterns. In image retrieval stage, the image training and retrieval process is treated as searching for an ordered optimal cycle in...
In the current era of digital communication, the use of digital images has grown high for expressing, sharing and interpreting information. While working with the digital images it is quite often that one needs to search for a specific image for a particular situation based on the visual contents of the image. Image retrieval by contents is one of the modern ways for searching huge digital image repositories...
This paper proposes a new self-adaptive feature extraction scheme to improve retrieval precision for Content-based Image Retrieval (CBIR) systems on mobile phones such that users can search similar pictures for a query image taken from their mobile phones. The proposed methods employ a newly modified extraction method using the Canny edge-based Edge Histogram Descriptor (CEHD), Color Layout Descriptor...
Dozens of image features have been proposed in recent decades, which could measure the similarity of images and promote the improvement of performance in image retrieval. Different features focus on different views of the image, where two image quite distant with one feature may close with another. In this paper, we attempt to integrate different measures together to improve the image retrieval accuracy...
Shape is one of the most important features in Content Based Image Retrieval (CBIR). When a shape is used as feature, edge detection might be the first step of feature extraction. Invariance to the different transformations like translation, rotation, and scale is required by a good shape representation. In the literature, a large number of shape representations and retrieval methods can be found...
Shape is one of the main features in content-based image retrieval (CBIR). This paper proposes a novel CBIR technique based on shape feature. This technique uses the distances between the boundary points of a shape and the smallest rectangle that covers it. The proposed technique is a Fourier based technique and it is invariant to translation, scaling and rotation. The retrieval performance between...
Typical image features such as color, shape, and texture are used in content-based image retrieval. Retrieval which uses only one image feature has worse performance in case where the content of an image is complex or where a database contains many images. So, many approaches for integrating these features have been studied. However, the problem of these approaches is how to appropriately assign weighting...
Using content based image retrieval systems the images are compared on the grounds of the stored information of images. As simple descriptors the intensity (or color), the texture and the shape is widely used. Special property of thermal images - similarly to gray-scale images - the have only one intensity channel. But then these intensities change on an other way than in gray-scale images. In this...
In content-based image retrieval systems, relevance feedback is an important way to narrow the gap between low-level features and high-level conception. Relevant images were often considered in traditional relevant feedback. But irrelevant images weren't considered. The method of moving query vector and method of updating weight factor are only separately used in content-based image retrieval. A new...
Nowadays, with the rapid development of medical imaging technology and information technology more and more medical images are available. Medical image retrieval plays increasingly important role in medical applications. Content-based medical image retrieval (CBMIR) poses unique challenges due to the unique characteristics of medical images. While current research of CBMIR focuses on low-level visual...
The usefulness of an image database depends on whether the image of interest can be easily located. Feature extraction is a crucial step of image retrieval. The well known SIFT descriptor is a keypoint based image feature. It can be used to robustly find the same objects in different images, i.e., achieve the object recognition task. However, it is not effective on the image retrieval task, i.e.,...
Local boundary information plays an important role in shape description. In this paper, an improved Arc-Height Function is presented for shape description and retrieval. The Improved Arc-Height is independent of centroid distance (CD) and can capture local boundary information very accurately. And an effective fusion strategy is utilized to integrate Fourier features derived from the improved Arc-Height...
In this work for CBIR system, all the image feature descriptors including color descriptors, texture descriptors and shape descriptors are used to represent low-level image features. Implementation of one feature descriptor doesn't give sufficient retrieval accuracy. For combining of different types of features, there is a need to train these features with different weights to achieve good results...
This paper proposes a new descriptor for radiological image retrieval. The proposed approach is based on fuzzy shape contexts, Fourier transforms and Eigenshapes. At first, fuzzy shape context histograms are computed. Then, a 2D FFT is performed on each 2D histogram to achieve rotation invariance. Next, histograms are projected onto a lower dimensionality feature space. The new space is more representative...
Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature...
Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. Here novel framework for combining all the three i.e. color, texture and shape information, to achieve higher retrieval efficiency is presented. The image is partitioned into non overlapping tiles of equal size. The color moments and geometric moments serve as local descriptors...
This paper proposes a multiclass image retrieval method using combined color-frequency-orientation histogram. Shape information, obtained via edge detector and Hough Transform, is also incorporated into the new feature. The feature has shown advantage in both unsupervised and supervised learning on Corel image dataset containing 10 categories of 1000 complex scenes. In unsupervised learning, comparing...
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