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The issue of near duplicate document image retrieval is addressed in this paper, which is characterized by not only encoding each individual word in the image but also modeling its local spatial configuration. On representing each word in the image as a string in terms of its shape characteristics, a lexicon is first learnt from a training set. Then a word in an arbitrary document image can be soft...
In this paper we use the Earth Movers Distance (EMD) algorithm to measure similarity between shapes for recognizing and searching Arabic words. We have used the Shape Context and the Angular Radial Partitioning descriptors to evaluate matching and recognizing with EMD. Based on the encouraging results of high accuracy and recall, we follow the low-distortion embedding of the Earth Mover's Distance...
In this paper, we propose a fast large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbours for query signatures. In the first stage, we use M randomly generated hyper planes to separate shape context feature points into different bins, and...
The main objective of Content-Based Image Retrieval (CBIR) systems is to retrieve a ranked list containing the most similar images of a collection given a query image, by taking into account their visual content. Although these systems represent a very promising approach, in many situations is very challenging to assure the quality of returned ranked lists. Supervised approaches rely on training data...
We introduce a statistical shape descriptor for Sketch-Based Image Retrieval. The proposed descriptor combines feature information in near and far support regions defined for each sketch point. Two feature values are extracted from each point, corresponding to near and far support regions from the point's perspective, and used to populate a 2-D histogram representing the shape features of the sketch...
Efficacious retrieval of similar shapes from large image databases is still a challenging problem. In recent works about shape retrieval, methods based on Dynamic Space Warping (DSW) and descriptors with contour information have had a significant presence. This paper introduces a technique for Content-Based Image Retrieval (CBIR) that encompasses a robust corner detector and a new shape descriptor...
Digital images have many applications in different fields like medical imaging and diagnostics, weather forecasting, space research, military etc. The number of images available and their wide variety increases with the ease of acquiring, storing and sharing digital images due to the advances in technology. As a result, the significance of image retrieval algorithms and systems has been considerably...
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 many applications, we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples. Often the pairwise similarities (which are also called affinities) are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples. As observed in several recent approaches,...
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...
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