Effective Image Retrieval Research Articles

Content-based image retrieval using spatial-color and Gabor texture on a mobile device

Mobile image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes a new efficient and effective mobile image retrieval method that applies a weighted combination of color and texture utilizing spatial-color and second order statistics. The system for mobile image searches runs in real-time on an iPhone and can easily be used to find a specific image. To evaluate the performance of the new method, we assessed the Xcode simulations performance in terms of average precision and F-score using several image databases and compare the results with those obtained using existing methods such as MPEG-7. Experimental trials revealed that the proposed descriptor exhibited a significant improvement of over 13% in retrieval effectiveness, compared to the best of the other descriptors.

Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain

The effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantization in a compressed domain.

CBIR using Textural Feature

CBIR system focuses on retrieving images from the database; the system depends on the way the indexing is being implemented. The way or method in which an image is stored will affect how it will be retrieved later and which can save more storage space and improve the retrieval process. Building effective content-based image retrieval (CBIR) systems involves the combination of image creation, storage, security, transmission, analysis, evaluation feature extraction, and feature combination in order to store and retrieve images effectively. The goal of CBIR systems is to support image retrieval based on content i.e. shape, color, texture. In this paper we have implemented CBIR techniques using conventional Histogram and Gabor filter. We have shown results of query image and retrieved image also 2D frequency response of Gabor filter with various angles as it is direction dependent filter. We have used Euclidean distance as a measure to calculate distance between two images.

Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Since shape is one of the important low level features of any Content Based Image Retrieval (CBIR) system, this paper proposes a new edge based shape feature representation method with multiresolution enhanced orthogonal polynomials model and morphological operations for effective image retrieval. In the proposed method, initially the orthogonal polynomials model coefficients are computed and reordered into multiresolution subband like structure. Edge image is then obtained by utilizing the two level adaptive thresholds and local maxima of the gradient in horizontal, vertical, diagonal and anti-diagonal directions. The approximate shape boundary of the image is recovered with morphological operations. Then the Pseudo Zernike moment based global shape features, which are invariant to basic geometric transformations, are extracted. The obtained features are termed as global shape feature vector and are used for retrieving similar images with Canberra distance metric. The efficiency of the proposed method is experimented on a subset of standard Corel, Yale and MPEG-7 databases and the results are compared with existing techniques.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

An effective image retrieval using the fusion of global and local transforms based features

Retrieval efficiency and accuracy are the important issues in designing content based image retrieval system. Thus, in this paper, we propose an effective descriptor based on angular radial transform (ART) and polar Hough transform (PHT), which is capable of fulfilling the above requirements. ART is used as a region based shape descriptor, which represents the global aspects of an image. PHT is used as a local shape descriptor for detecting linear edges in an edge image. The detected linear edges represent the association among adjacent edge points. The perpendicular distance of each linear edge to the centroid of the edge image is computed to build histograms, which exhibit rotation, scale and translation invariant properties. The combination of both global (ART based) and local (PHT based) features yields improved retrieval accuracy, which is analyzed over various sorts of image databases. An extensive set of experiments witness the superiority of the proposed hybrid system over other major contour based, region based and hybrid approaches.

Integrating wavelets with clustering and indexing for effective content-based image retrieval

Recent development in technology influenced our daily life and the way people communicate and store data. There is a clear shift from traditional methods to sophisticated techniques; this maximizes the utilization of the widely available digital media. People are able to take photos using hand held devices and there is a massive increase in the volume of photos digitally stored. Digital devices are also shaping the medical field. Scanners are available for every part of the body to help identifying problems. However, this tremendous increase in the number of digitally captured and stored images necessitates the development of advanced techniques capable of classifying and effectively retrieving relevant images when needed. Thus, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community is competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical monitoring applications in homeland security, scientific and medical domains, among others. All of this motivated for the work described in this paper. We propose a novel approach which uses a well-known clustering algorithm k-means and a database indexing structure B+-tree to facilitate retrieving relevant images in an efficient and effective way. Cluster validity analysis indexes combined with majority voting are employed to verify the appropriate number of clusters. While searching for similar images, we consider images from the closest cluster and from other nearby clusters. We introduced two new parameters named cG and cS to determine the distance range to be searched in each cluster. These parameters enable us to find similar images even if the query image is misclustered and to further narrow down the search space for large clusters. To determine values of cG and cS, we introduced a new formula for gain measurement and we iteratively find the best gain value and accordingly set the values. We used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are promising. The results demonstrate how using data mining techniques could improve the efficiency of the CBIR task without sacrificing much from the accuracy of the overall process.

The Mosaic Test: measuring the effectiveness of colour-based image retrieval

A variety of content-based image retrieval systems exist which enable users to perform image retrieval based on colour content—i.e., colour-based image retrieval. For the production of media for use in television and film, colour-based image retrieval is useful for retrieving specifically coloured animations, graphics or videos from large databases (by comparing user queries to the colour content of extracted key frames). It is also useful to graphic artists creating realistic computer-generated imagery (CGI). Unfortunately, current methods for evaluating colour-based image retrieval systems have 2 major drawbacks. Firstly, the relevance of images retrieved during the task cannot be measured reliably. Secondly, existing methods do not account for the creative design activity known as reflection-in-action. Consequently, the development and application of novel and potentially more effective colour-based image retrieval approaches, better supporting the large number of users creating media for use in television and film productions, is not possible as their efficacy cannot be reliably measured and compared to existing technologies. As a solution to the problem, this paper introduces the Mosaic Test. The Mosaic Test is a user-based evaluation approach in which participants complete an image mosaic of a predetermined target image, using the colour-based image retrieval system that is being evaluated. In this paper, we introduce the Mosaic Test and report on a user evaluation. The findings of the study reveal that the Mosaic Test overcomes the 2 major drawbacks associated with existing evaluation methods and does not require expert participants.

Medical Image Retrieval: Past and Present

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.

A NOVEL TWO-LEVEL COLOR IMAGE RETRIEVAL METHOD

This paper presents an effective two-level color image retrieval method based on the RGB color model. For the purpose of effectively retrieving more similar images from the digital image databases, we divide the image into different regions and set bigger weight for the region we focus on. In addition, we set different weights for each RGB component of the color image according to the main hue of it. As a result, this scheme can enhance the retrieval accuracy that is measured in terms of the recall and precision.

Effective venue image retrieval using robust feature extraction and model constrained matching for mobile robot localization

This paper describes a novel system for mobile robot localization in an indoor environment, using concepts like homography and matching borrowed from the context of stereo and content-based image retrieval techniques (CBIR). To deal with variations with respect to viewpoint and camera positions, a group of points of interest (POI) is extracted to represent the image for robust matching. To cope with illumination changes, we propose to produce a contrast image for each video frame by using the root mean square strategy, thus all the POIs are extracted from the corresponding contrast images to provide perceptually consistent measurement of image content. To achieve effective image matching, modeling of robot behavior for model constrained matching is proposed, where normalized cross correlation is employed for local matching to determine corresponding POI pairs followed by homography based global optimization using RANSAC. Meanwhile, application of specific constraints also helps to exclude irrelevant frames in the training set to further improve the efficiency and robustness. The proposed approach has been successfully applied to the Robot Vision task for the ImageCLEF workshop, and the experimental results have fully demonstrated the high-quality performance of our approaches in terms of both precision and robustness. The system and approach outlined in this paper was ranked the second best in the optional task group in ImageCLEF 2009. In addition to demonstrating the merits of our approach in isolation, we also illustrate the benefits of our proposed approach in comparison with other submissions.

An effective method for color image retrieval based on texture

This paper presents an effective color image retrieval method based on texture, which uses the color co-occurrence matrix to extract the texture feature and measure the similarity of two color images. Due to the color information such as components and distribution is also taken into consideration, the feature obtained not only reflects the texture correlation but also represents the color information. As a result, our proposed method is superior to the gray-level co-occurrence matrix method and color histogram method, and it enhances the retrieval accuracy which is measured in terms of the recall and precision in the meanwhile.

Effective image retrieval techniques based on novel salient region segmentation and relevance feedback

This paper proposes an effective region-based image retrieval technique based on novel salient region segmentation and relevance feedback. With a good and fast segmentation technique, our system achieves an on-the-fly segmentation capability, which enables users to select particular regions for matching and feedbacks without waiting for image segmentation. Therefore, we adopt a relatively simple feedback schemes to derive the intent of the user. The experimental results show that the system performance is greatly improved with this capability. Furthermore, a Quick-match algorithm is also presented in this paper. The mechanism of the Quick-match algorithm is to exclude from distance computation regions that are of low possibility to be the top-Mmatches. This algorithm excludes most of regions from distance computation and therefore greatly cuts down the turnaround time of the retrieval with slightly degradation of precision.

SU‐GG‐I‐153: A Novel Relevance Feedback Approach for Efficient Mammogram Image Retrieval

Purpose: With the rapid increase of medical databases and PACS (picture archiving and communication systems) image volumes, it became critical to develop new tools to retrieve images from these archives more effectively and efficiently for routine clinical practice. A key factor for successful image retrieval systems lies in the development of efficient methods that can derive high‐level semantic concepts from low‐level image content features. Despite many efforts, an efficient retrieval system for medical images still remains a challenging problem due to their complex contents in relation to disease conditions. Towards this goal we have developed a machine learning framework that utilizes expert's knowledge for online retrieval of relevant mammogram images based on clustered microcalcifications. Method and Materials: We tested a database of 200 clinical mammograms that were taken from 104 patients with known pathology. To automatically retrieve mammograms from the database that are perceptually similar to a given query image, we proposed an efficient relevance feedback algorithm based on support vector machine (SVM) regression and incremental learning that dramatically reduces the processing time, avoiding the retraining of the retrieval system. Results: We conducted various experiments to evaluate the developed algorithm under varying user's interactions. It was observed that our proposed method showed significant superiority over off‐line learning. In particular, in a matching fraction test where the top‐k retrieved images were compared with the disease condition of the query image (benign or malignant) after the user's relevance feedback, the proposed method achieved a matching fraction as high as 82.4%. Conclusions: We have developed a novel relevance feedback method for effective retrieval of relevant mammogram images. Our experimental results demonstrated that the proposed method is not only better in performance, but more computationally efficient than offline SVM based on re‐training, while maintaining its effectiveness for online clinical application.

Content-based Search for Effective Image Retrieval

Content-based Search for Effective Image Retrieval

Recognition of attentive objects with a concept association network for image annotation

With the advancement of imaging techniques and IT technologies, image retrieval has become a bottle neck. The key for efficient and effective image retrieval is by a text-based approach in which automatic image annotation is a critical task. As an important issue, the metadata of the annotation, i.e., the basic unit of an image to be labeled, has not been fully studied. A habitual way is to label the segments which are produced by a segmentation algorithm. However, after a segmentation process an object has often been broken into pieces, which not only produces noise for annotation but also increases the complexity of the model. We adopt an attention-driven image interpretation method to extract attentive objects from an over-segmented image and use the attentive objects for annotation. By such doing, the basic unit of annotation has been upgraded from segments to attentive objects. Visual classifiers are trained and a concept association network (CAN) is constructed for object recognition. A CAN consists of a number of concept nodes in which each node is a trained neural network (visual classifier) to recognize a single object. The nodes are connected through their correlation links forming a network. Given that an image contains several unknown attentive objects, all the nodes in CAN generate their own responses which propagate to other nodes through the network simultaneously. For a combination of nodes under investigation, these loopy propagations can be characterized by a linear system. The response of a combination of nodes can be obtained by solving the linear system. Therefore, the annotation problem is converted into finding out the node combination with the maximum response. Annotation experiments show a better accuracy of attentive objects over segments and that the concept association network improves annotation performance.

An effective image retrieval scheme using color, texture and shape features

In this paper, we present a new and effective color image retrieval scheme for combining all the three i.e. color, texture and shape information, which achieved higher retrieval efficiency. Firstly, the image is predetermined by using fast color quantization algorithm with clusters merging, and then a small number of dominant colors and their percentages can be obtained. Secondly, the spatial texture features are extracted using a steerable filter decomposition, which offers an efficient and flexible approximation of early processing in the human visual system. Thirdly, the pseudo-Zernike moments of an image are used for shape descriptor, which have better features representation capabilities and are more robust to noise than other moment representations. Finally, the combination of the color, texture and shape features provide a robust feature set for image retrieval. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.

An effective image retrieval mechanism using family-based spatial consistency filtration with object region

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of “TREC Video Retrieval Evaluation 2005 (TRECVID2005)”. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.

Semi-Supervised Learning Model Based Efficient Image Annotation

Automatic image annotation is a promising way to achieve more effective image management and retrieval. However, system performances of the existing state-of-the-art keyword annotation schemes are often not so satisfactory. Therefore, image annotation refinement is crucial to improve the imprecise annotation results. In this paper, a novel approach is developed to automatically annotate image content by a semi-supervised learning model. With perceptual visual characteristics, the candidate annotations of unlabelled images are first obtained based on a progressive model. Then, a transducitive model, random walk with restart algorithm is used to refine these candidate annotations and the top ones are reserved as the final annotations. Experiments conducted on the typical Corel dataset show the effectiveness of the proposed approach.

Developing and implementing a sparse ontology with a visual index for personal photograph retrieval

The advent of digital cameras has provided photographers, with varying levels of expertise, the opportunity to accumulate large repositories of digital images. However, this expansion has also brought the attendant difficulty of image retrieval. This paper reviews the considerable work already carried out on image retrieval and identifies critical constraints in attempting to handle the underlying semantics of photographic images. The authors address the issue of how an amateur photographer, storing several thousand images a year, can effectively and efficiently manage a personal collection. A number of surprisingly simple techniques are proposed utilising a sparse lexical ontology alongside iconic visual clues to identify key representative events in the collection. The photographer’s associated knowledge and experience, often shared by others who are attuned to the context of the collection, are critical to the effective management of the images. The authors recommend the use of existing browsers and a simple keyword table to facilitate concise and effective image storage and retrieval. Photographers can further enrich the representation of their collection by clustering images in accordance with their perceived relevance and aesthetic qualities. The authors argue that these properties can also be exploited to enhance retrieval.