Content-based Image Retrieval Method Research Articles

Integrated color, texture and shape information for content-based image retrieval

Feature extraction and the use of the features as query terms are crucial problems in content-based image retrieval (CBIR) systems. The main focus in this paper is on integrated color, texture and shape extraction methods for CBIR. We have developed original CBIR methodology that uses Gabor filtration for determining the number of regions of interest (ROIs), in which fast and effective feature extraction is performed. In the ROIs extracted, texture features based on thresholded Gabor features, color features based on histograms, color moments in YUV space, and shape features based on Zernike moments are then calculated. The features presented proved to be efficient in determining similarity between images. Our system was tested on postage stamp images and Corel photo libraries and can be used in CBIR applications such as postal services.

Content-based object organization for efficient image retrieval in image databases

Much research has focused on content-based image retrieval (CBIR) methods that can be automated in image classification and query processing. In this paper, we propose a blob-centric image retrieval scheme based on the blobworld representation. The blob-centric scheme consists of several newly proposed components, including an image classification method, an image browsing method based on semantic hierarchy of representative blobs, and a blob search method based on multidimensional indexing. We present the database structures and their maintenance algorithms for these components and conduct a performance comparison of three image retrieval methods, the naive method, the representative-blobs method, and the indexed-blobs method. Our quantitative analysis shows significant reduction in query response time by using the representative-blobs method and the indexed-blobs method.

IMAGE RETRIEVAL BASED ON REGION SEPARATION AND MULTIRESOLUTION ANALYSIS

In this paper, a simple and fast querying method for content-based image retrieval is represented. Using the multispectral gradient, a color image is split into two disjoint parts that are the homogeneous color regions and the edge regions. The homogeneous regions are represented by the traditional color histograms, and the edge regions are represented by the multispectral gradient module mean histograms. In order to measure the similarity degree between two color images both quickly and effectively, we use a one-dimensional pseudo-metric, which makes use of the one-dimensional Daubechies decomposition and compression of the extracted histograms. Our querying method is invariant to the query color image object translations and color intensities. The experimental results are reported on a collection of 10,000 LAB color images.

Fully automatic coloring of grayscale images

This paper introduces a methodology for adding color to grayscale images in a way that is completely automatic. Towards this goal, we build on a technique that was recently developed to transfer colors from a user-selected source image to a target grayscale image. More specifically, in order to eliminate the need for manual selection of the source image, we use content-based image retrieval methods to find suitable source images in an image database. To assess the merit of our methodology, we performed a survey where volunteers were asked to rate the plausibility of the colorings generated automatically for grayscale images. In most cases, automatically-colored images were rated either as totally plausible or as mostly plausible.

영역 특징벡터를 이용한 내용기반 영상검색

본 논문은 기존의 컬러 히스토그램 방법들의 단점을 극복하고자 영역 특징백터를 이용한 영상 검색 방법을 제안한다. 컬러 히스토그램 검색방법들은 양자화 오류 등의 이유로 정확성이 떨어지는 단점이 있다 이를 해결하기 위해 제안 방법은 색상 정보를 HSY 공간으로 변환하여 순수 색상 정보인 hue 성분만을 양자화하여 히스토그램을 구하고, 이를 명암, 이동, 회전등에 강인한 검색 특징으로 사용한다. 또한 컬러 히스토그램 방법들의 가장 큰 문제점인 공간 정보가 부족한 것은 영상을 16개 영역으로 나눠서 각 영역간의 비교를 통해 해결한다. 그리고 색상 검색에 추가적으로 모양 특징인 에지와 질감 특징인 DCT 변환의 DC를 이용하여 검색의 정확도를 높인다 1,000개의 컬러 영상을 사용해 실험한 결과 기존의 방법들 보다 좋은 정확성을 보인다. This paper proposes a method of content-based image retrieval using region feature vector in order to overcome disadvantages of existing color histogram methods. The color histogram methods have a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV space and quantize hue factor being purecolor information and calculate histogram and then use thus for retrieval feature that is robust in brightness, movement, and rotation. Also we solve an insufficient part that is the most serious problem in color histogram methods by dividing an image into sixteen regions and then comparing each region. We improve accuracy by edge and DC of DCT transformation. As a result of experimenting with 1,000 color images, the proposed method has showed better precision than the existing methods.

A semantic learning for content-based image retrieval using analytical hierarchy process

In this paper, a new semantic learning method for content-based image retrieval using the analytic hierarchical process (AHP) is proposed. AHP proposed by Satty used a systematical way to solve multi-criteria preference problems involving qualitative data and was widely applied to a great diversity of areas. In general, the interpretations of an image are multiple and hard to describe in terms of low-level features due to the lack of a complete image understanding model. The AHP provides a good way to evaluate the fitness of a semantic description used to interpret an image. According to a predefined concept hierarchy, a semantic vector, consisting of the fitness values of semantic descriptions of a given image, is used to represent the semantic content of the image. Based on the semantic vectors, the database images are clustered. For each semantic cluster, the weightings of the low-level features (i.e. color, shape, and texture) used to represent the content of the images are calculated by analyzing the homogeneity of the class. In this paper, the values of weightings setting to the three low-level feature types are diverse in different semantic clusters for retrieval. The proposed semantic learning scheme provides a way to bridge the gap between the high-level semantic concept and the low-level features for content-based image retrieval. Experimental results show that the performance of the proposed method is excellent when compared with that of the traditional text-based semantic retrieval techniques and content-based image retrieval methods.

Visual Feature Extraction under Wavelet Domain for Image Retrieval

In this paper, we propose a new visual feature extraction method for content-based image retrieval (CBIR) based on wavelet transform which has both spatial-frequency and multi-resolution characteristics. We extract visual features for each frequency band in wavelet transformation and use them for CBIR. The lowest frequency band involves utilizing the spatial information of an original image. We extract 64 feature vectors using fuzzy homogeneity in the wavelet domain, which considers both the wavelet coefficients and the spatial information of each coefficient. In addition, we extract 3 feature vectors using the energy values of high frequency bands, and store those to the image database. As a query, we retrieve the most similar image from the image database according to the 10 largest homograms (normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

In this paper, we examine the complexities involved in retrieving images from a database comprised of objects of very similar appearance. Such an operation requires a process that can discriminate among images at a very fine level, such as distinguishing among various species of fish. Furthermore, incidental environmental factors such as change in viewpoints and slight, nonessential shape deformation must be excluded from the similarity criteria. To this end, we propose a new method for content-based image retrieval and indexing, one that is well suited for discriminating among objects within the same class in a way that is insensitive to incidental environmental changes. The scheme comprises a global alignment and a local matching process. Affine transform is used to model the different viewpoints associated with positioning the camera, while multi-dimensional indexing techniques are used to make the global alignment scheme efficient. A local matching process based on dynamic programming allows the optimal matching of local structures using cost metrics that may ignore nonessential local shape deformation. Results show the method's ability to cancel out visual distortions caused by a changing viewpoint, and its tolerance to noise, occlusion, and slight deformations of the object.

Context-dependent segmentation and matching in image databases

The content of an image can be summarized by a set of homogeneous regions in an appropriate feature space. When exact shape is not important, the regions can be represented by simple “blobs.” Even for similar images, the blob representation of the two images might vary in shape, position, the number of blobs, and the represented features. In addition, separate blobs in one image might correspond to a single blob in the other image and vice versa. In this paper we present the BlobEMD framework as a novel method to compute the dissimilarity of two sets of blobs while allowing for context-based adaptation of the image representation. This results in representations that represent well the original images but at the same time are best aligned with respect to the representations of the context images. Similarly, we can perform image segmentation where the segmentation of an image is guided by a reference image. This novel approach makes segmentation a context-based task. We compute the blobs by using Gaussian mixture modeling and use the Earth mover’s distance (EMD) to compute both the dissimilarity of the images and the flow-matrix of the blobs between the images. The BlobEMD flow-matrix is used to find optimal correspondences between source and target image representations and to adapt the representation of the source image to that of the target image. This allows for similarity measures between images that are insensitive to the segmentation process and to different levels of details of the representation. We show applications of this method for content-based image retrieval, image segmentation, and matching models of heavily dithered images with models of full resolution images.

Multi-class relevance feedback content-based image retrieval

Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two-class relevance feedback: relevant and irrelevant classes. While simple computationally, two-class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. In this paper we propose a multi-class form of relevance feedback retrieval to try to exploit multi-class information. For a given query, we use a χ 2 analysis to determine the local relevance of each feature dimension with multi-class relevance feedback. This information is then used to customize the retrieval metric to rank images. By exploiting multi-class information, our method is able to create flexible metrics that better capture user perceived similarity. In a number of image data sets, the method achieves a higher level of precision with fewer iterations, demonstrating the potential for substantial improvements over two-class relevance feedback retrieval.

Efficient Content-Based Image Retrieval through Metric Histograms

This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique works on each image independently from the others in the dataset, therefore there is no pre-defined number of color regions in the resulting histogram. Thus, it is not possible to use traditional comparison algorithms such as Euclidean or Manhattan distances. To allow the comparison of images through the new feature vectors given by metric histograms, a new metric distance function MHD( ) is also proposed. This paper shows the improvements in timing and retrieval discrimination obtained using metric histograms over traditional ones, even when using images with different spatial resolution or thumbnails. The experimental evaluation of the new method, for answering similarity queries over two representative image databases, shows that the metric histograms surpass the retrieval ability of traditional histograms because they are invariant on geometrical and brightness image transformations, and answer the queries up to 10 times faster than the traditional ones.

Query-by-Gesture: An Alternative Content-Based Image Retrieval Query Scheme

Current research on content-based image retrieval (CBIR) is centered on designing efficient query schemes in order to provide a user with effective mechanisms for image database search. Among representative CBIR query schemes, query-by-sketch has been one of the attractive query tools that are highly adaptive to user's subjectivity. However, query-by-sketch has a few limitations. That is, most sketch tools demand expertise in image processing or computer vision of the user to provide good enough sketches that can be used as query. Furthermore, sketching the exact shape of an object using a mouse can be a burden on the user. To overcome some of the limitations associated with query-by-sketch, we propose a new query method for CBIR, query-by-gesture, that does not require sketches, thereby minimizing user interaction. In our system, the user does not need to use a mouse to make a sketch. Instead, the user draws the shape of the object that heshe intends to search in front of a camera by hand. In addition, our query-by-gesture technique uses relevance feedback to interactively improve retrieval performance and allow progressive refinement of query results according to the user's specification. The efficacy of our proposed method is validated using images from the Corel-Photo CD.

A Survey on Current Content based Image Retrieval Methods

Retrieving information from the Web is becoming a common practice for internet users. However, the size and heterogeneity of the Web challenge the effectiveness of classical information retrieval techniques. Content-based retrieval of images and video has become a hot research area. The reason for this is the fact that we need effective and efficient techniques that meet user requirements, to access large volumes of digital images and video data. This paper gives a brief survey of current CBIR (Content Based Image Retrieval) methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of CBIR, image feature representation and extraction, Multidimensional indexing. Furthermore future research directions are suggested.

엔트로피에 기반한 영상분할을 이용한 영상검색

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n<TEX>$\times$</TEX>n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

Planar shape indexing and retrieval based on Hidden Markov Models

This paper presents a planar shape recognition method for content-based image retrieval in multimedia databases. Shapes are characterised by their corners, which are extracted from the curvature function of their contours. Then, corner sequences are compared by using Hidden Markov Models so that the process is resistant against corner shifting and loss. The proposed method has been successfully tested over publicly available databases.

Image indexing by Color Plane Moment

AbstractAmong the features of content‐based image retrieval, color features provide important clues to search similar image contents. In addition to color, there exists more information in the image. In this article, we propose a new method for content‐based image retrieval: Color Plane Moment (CPM). This method combines colors' content and their spatial distribution to improve image query results. It integrates the ideas of color histogram, backprojection, and moments. The CPM uses backprojection as one of the image preprocessing methods and computes the invariant moments with those dominant color plane images after preprocessing. There are several dominant colors chosen by color histogram in an image, and each can be expressed by seven invariant moment values that represent spatial distribution of those dominant color planes, respectively. Simulation results demonstrate that the proposed technique outperforms the other techniques. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 139–148, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10022

Visual information retrieval system via content-based approach

In this paper a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content is proposed. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. A new indexing method that supports fast retrieval in large image databases is also presented. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.2 percent of the images from direct comparison.

Independent feature analysis for image retrieval

Content-based image retrieval methods based on the Euclidean metric expect the feature space to be isotropic. They suffer from unequal differential relevance of features in computing the similarity between images in the input feature space. We propose a learning method that attempts to overcome this limitation by capturing local differential relevance of features based on user feedback. This feedback, in the form of accept or reject examples generated in response to a query image, is used to locally estimate the strength of features along each dimension while taking into consideration the correlation between features. This results in local neighborhoods that are constricted along feature dimensions and that are most relevant, while elongated along less relevant ones. In addition to exploring and exploiting local principal information, the system seeks a global space for efficient independent feature analysis by combining such local information. We provide experimental results that demonstrate the efficacy of our technique using both simulated and real-world data.

Integrated spatial and feature image query

We present a new system for querying for images by regions and their spatial and feature attributes. The system enables the user to find the images that contain arrangements of regions similar to those diagrammed in a query image. By indexing the attributes of regions, such as sizes, locations and visual features, a wide variety of complex joint spatial and feature queries are efficiently computed. In order to demonstrate the utility of the system, we develop a process for the extracting color regions from photographic images. We demonstrate that integrated spatial and feature querying using color regions improves image search capabilities over non-spatial content-based image retrieval methods.

Efficient Content-Based Image Retrieval Method Using Color and Texture

In this paper, we propose efficient content-based image retrieval methods using the automatic extraction of the low-level visual features as image content. Two new feature extraction methods are presented. The first one is an advanced color feature extraction derived from the modification of Stricker's method. The second one is a texture feature extraction using some DCT coefficients which represent some dominant directions and gray level variations of the image. In the experiment with an image database of 200 natural images, the proposed methods show higher performance than other methods. They can be combined into an efficient hierarchical retrieval method.