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Growth of the image mining arena calls for the need of quality image retrieval techniques in par with the human perception which are invariant to scale and rotation. An optimized content based image retrieval system based on local visual attention features to bridge the semantic gap problem is proposed. The approach involves the salient point detection using Scale Up Robust Features (SURF) detector...
Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry...
This study proposes an algorithm where edge shape information is retrieved using a vote based rearranged chain code. The proposed algorithm rearranges and normalizes the chain code of edges to create a maximum vote based normalization chain code whose correlativity is reinforced. As it obtains cyclical maximum values from the chain code, rearranges them and flips code values according to the frequency,...
In the area of retrieving image databases, one of the promising approaches is to retrieve it by specifying image example. However, specifying a single image example is not always sufficient to get satisfactory result, since one image example does not give comprehensive ranges of values that reflect the various aspects of the object to be retrieved. In this paper, we propose a method of retrieving...
With the emerge of multimedia, communication and processing, a typical image database has a large volume of data. It is an essential to build an efficient retrieval system to browse through the entire database. In this paper we proposed precise Relevance Feed Back (RFB) Content Based Image Retrieval (CBIR) using multiple features based on interactive retrieval approach which will extensively reduces...
Image Retrieval is a technique of searching, browsing, and retrieving the images from an image database. There are two types of different image retrieval techniques namely text based image retrieval and content based image retrieval techniques. Text-Based image retrieval uses traditional database techniques to manage images. Content-based image retrieval (CBIR) uses the visual features of an image...
Image retrieval techniques are text based or content based. In the Text based image retrieval, the images are annotated and the database management system retrieves them. However, adding annotations and textual attributes through human intervention, is difficult and time consuming. Content Based Image Retrieval (CBIR) is a better alternative to this traditional text based image retrieval.
Content-Based Image Retrieval technique uses three primitive features like color, texture and shape which play a vital role in image retrieval. This paper presents a novel framework using color and shape features by extracting the different components of an image using the Lab and HSV color spaces to retrieve the edge features. Invariant moments are then used to recognize the image. In this proposed...
The effectiveness of the content-based image retrieval can be enhanced using the heterogeneous features embedded in the images. However, since the features in color and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrieval on heterogeneous features is a non-trivial task. In this paper, present a semantics-based...
With the explosion in demand for visual information retrieval in soccer videos, many Content-Based Video Retrieval (CBVR) models were born. However these current CBVR models still remain some shortcomings such as inflexible retrieval frameworks because they are majorly based on a specific training data set and a specific language. In this paper we propose a framework which contains flexible and easy-to-use...
At presents, the technique of multi-feature combination of trademark image retrieval is an increasingly popular branch of CBIR. In this paper, it's proposed that multi-feature combination based on color and shape is applied to trademark image retrieval. Firstly, RGB color space is converted to HSV color space, which complied with human visual perception better. Then color moments, which can describe...
In this paper content based image retrieval of x-ray images using fusion of spectral & shape features is discussed. Texture analysis and shape description are two of the key parts of image content description. Most of the existing descriptors are usually either application dependent or non-robust so we have used spectral measure for content analysis and Fourier descriptors for shape analysis....
Feature extraction is one of the most important issues for designing content-based image retrieval systems. In this paper, we present a method of extracting feature to retrieve images. In the first step of the proposed method, bound is introduced to mark the contour of the object. The center and the circumcircle of the object can be obtained from the bound. The principal angle of the object is computed...
In this paper we focus on the detection of inaccuracies in the results of content-based image analysis. During the analysis process we detect a set of features, which are later used in Image Retrieval. This detection is based on multiple algorithms specific to particular features. These algorithms use parameters, which have been obtained by the analysis of our test collection. However it seems that...
Content-based image retrieval relies on the use of efficient and effective image descriptors. One of the most important components of an image descriptor is concerned with the distance function used to measure how similar two images are. This paper presents a clustering approach based on distances correlation for computing the similarity among images. Conducted experiments involving shape, color,...
In order to solve the problem of millions image retrieval in science and technology resources database, the authors firstly studied extraction technologies of bottom image features in CBIR. Sub-block histogram method was used in the color feature extraction, and the Gabor wavelet transformation method were used in the texture feature extraction, while the invariant moment method was used in the shape...
Identification of similar trademarks is important in trademark registration. Shape feature could intuitively and effectively describes an object in a given image. Therefore, shape feature plays an important role in content-based image retrieval (CBIR) systems. The shape feature is particularly suitable for trademark image retrieval (TIR) systems. In this paper, we propose an effective solution for...
The content based image retrieval system may discovery user needed image fleetly and accurately from image database. In order to improve content based image retrieval system performance, this paper introduces a new method which realizes image retrieval by multi-feature fusion. Color feature is extracted based on color histogram, texture feature is extracted based on gray co-occurrence matrix, and...
Requirement of reduction of feature space of visual descriptors gets attention due to negative effects of high dimensional feature space. This paper reports the performance of Compacted Dither Pattern Code (CDPC) over Principal Component Analysis (PCA) based compact colour descriptor. There are several competitive advantages of CDPC in feature extraction and classification stages when compared to...
Active learning methods have attracted many researchers in the content-based image retrieval (CBIR) community. In this paper, we propose an efficient kernel-based active learning strategy to improve the retrieval performance of CBIR systems using class probability distributions. The proposed method learns for each class a nonlinear kernel which transforms the original feature space into a more effective...
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