Content-based Image Retrieval Tasks Research Articles

Semantic Based Image Annotation Using Descriptive Features and Retagging approach

The Semantic based annotation of an image is very important and a difficult task in content-based image retrieval (CBIR). The low-level features of the images are described using color and the texture features and the proposed model is used for semantic annotation of images. Also the textual annotations or the tags with multimedia content are the most effective approaches to organize and to support search over digital images and multimedia databases. The quality of the tags was refined using Image retagging method. The process is given as a multi-path graph based problem, which in parallel identifies the visual content of the images, semantic correlation of the tags as well as the primary information provided by users. The image annotation preferred because as the countless images exist in our lives it is not possible to annotate them all by hand. And so annotation by computer is a potential and promising solution to this problem precisely. The ability to annotate images semantically based on the objects that they contain is essential in image retrieval as it provides the mechanism to take advantage of existing text retrieval systems.

Adaptive and efficient colour quantisation based on a growing self-organising map

Studies on colour quantisation have indicated that its applications range from the relaxation of displaying hardware constraints in early years to a modern usage of facilitating content-based image retrieval tasks. Among many alternatives, approaches based on neural network models are generally accepted to be able to produce quality results in colour quantisation. However, these approaches using n quantised neurons require O(n) for a full search strategy, which is inefficient when n becomes large. In view of this, we propose to incorporate a growing quadtree structure into a self-organising map (GQSOM) which reaches a search time O(log n). Specifically, the strategy of inheriting from parent neurons hierarchically facilitates a much more efficient and flexible learning process. Both theoretical and empirical studies have shown that our approach is adaptive in determining an appropriate number of quantised colours, and the performance is significantly improved without compromise of the quantisation quality.

Comparison of color representations for content-based image retrieval in dermatology

We compare the effectiveness of 10 different color representations in a content-based image retrieval task for dermatology. As features, we use the average colors of healthy and lesion skin in an image. The extracted features are used to retrieve similar images from a database using a k-nearest-neighbor search and Euclidean distance. The images in the database are divided into four different color categories. We measure the effectiveness of retrieval by the average percentage of retrieved images that belong to the same category as a query image. We found that the difference of the colors of lesion and healthy skin is a better color descriptor than the pair of these colors. We obtained the best results with the CIE-Lab color representation [75+/-3.8% (95% confidence interval) correct retrieval rate for k=11], followed by CIE-Luv and CIE-Lch. CIE-Lab is the most effective color space for content-based image retrieval of dermatological images. The difference of the colors of lesion and healthy skin in an image is a better color descriptor than the pair of these colors.

Shape feature extraction and description based on tensor scale

Tensor scale is a morphometric parameter that unifies the representation of local structure thickness, orientation, and anisotropy, which can be used in several computer vision and image processing tasks. In this article, we exploit this concept for binary images and propose a shape salience detector and a shape descriptor— Tensor Scale Descriptor with Influence Zones. It also introduces a robust method to compute tensor scale, using a graph-based approach—the Image Foresting Transform. Experimental results are provided, showing the effectiveness of the proposed methods, when compared to other relevant methods, such as Beam Angle Statistics and Contour Salience Descriptor, with regard to their use in content-based image retrieval tasks.

Farthest point distance: A new shape signature for Fourier descriptors

Shape description is an important task in content-based image retrieval (CBIR). A variety of techniques have been reported in the literature that aims to represent objects based on their shapes. Each of these techniques has its pros and cons. Fourier descriptor (FD) is one of these techniques a simple, yet powerful technique that offers attractive properties such as rotational, scale, and translational invariance. Shape signatures, which constitute an essential component of Fourier descriptors, reduce 2-D shapes to 1-D functions and hence facilitate the process of deriving invariant shape features using the Fourier transform. A good number of shape signatures have been reported in the literature. These shape signatures lack important shape information, such as corners, in their representations. This information plays a major role in distinguishing between different shapes. In this paper, we present the farthest point distance (FPD), a novel shape signature that includes corner information to enhance the performance of shape retrieval using Fourier descriptors. The signature is calculated at each point on a shape contour. This signature yields distances calculated between the different shape corners, and captures points within the shape at which the human focuses visual attention in order to classify shapes. To reach a comprehensive conclusion about the merit of the proposed signature, the signature is compared against eight popular signatures using the well-known MPEG-7 database. Furthermore, the proposed signature is evaluated against standard boundary- and region-based techniques: the curvature scale space (CSS) and the Zernike moments (ZM). The FPD signature has demonstrated superior overall performance compared with the other eight signatures and the two standard techniques.

An efficient algorithm for attention-driven image interpretation from segments

In the attention-driven image interpretation process, an image is interpreted as containing several perceptually attended objects as well as the background. The process benefits greatly a content-based image retrieval task with attentively important objects identified and emphasized. An important issue to be addressed in an attention-driven image interpretation is to reconstruct several attentive objects iteratively from the segments of an image by maximizing a global attention function. The object reconstruction is a combinational optimization problem with a complexity of 2 N which is computationally very expensive when the number of segments N is large. In this paper, we formulate the attention-driven image interpretation process by a matrix representation. An efficient algorithm based on the elementary transformation of matrix is proposed to reduce the computational complexity to 3 ω N ( N - 1 ) 2 / 2 , where ω is the number of runs. Experimental results on both the synthetic and real data show a significantly improved processing speed with an acceptable degradation to the accuracy of object formulation.

A framework and baseline results for the CLEF medical automatic annotation task

The medical automatic annotation task issued by the cross language evaluation forum (CLEF) aims at a fair comparison of state-of-the art algorithms for medical content-based image retrieval (CBIR). The contribution of this work is twofold: at first, a logical decomposition of the CBIR task is presented, and key elements to support the relevant steps are identified: (i) implementation of algorithms for feature extraction, feature comparison, and classifier combination, (ii) visualization of extracted features and retrieval results, (iii) generic evaluation of retrieval algorithms, and (iv) optimization of the parameters for the retrieval algorithms and their combination. Data structures and tools to address these key elements are integrated into an existing framework for image retrieval in medical applications (IRMA). Secondly, baseline results for the CLEF annotation tasks 2005–2007 are provided applying the IRMA framework, where global features and corresponding distance measures are combined within a nearest neighbor approach. Using identical classifier parameters and combination weights for each year shows that the task difficulty decreases over the years. The declining rank of the baseline submission also indicates the overall advances in CBIR concepts. Furthermore, a rough comparison between participants who submitted in only one of the years becomes possible.

Spatially consistent partial matching for intra- and inter-image prototype selection

This paper describes a method of introducing spatial consistency constraints in the process of matching set-based descriptors extracted from digital images. The proposed matching technique is guided by a rule that can be summarized as follows: a descriptor is important for the match if it is similar to some descriptor from the other image and its spatial neighbors are important. The resulting match is partial in the sense that it deliberately avoids the complexity of searching for one-to-one correspondences among particular descriptors, but established affinity among groups of descriptors instead. Formally, the proposed method is expressed as an eigenvalue problem, where the principal eigenvector's components render the importance values of individual descriptors, while the corresponding eigenvalue represents an estimate of the overall strength of affinity between images being matched. These measures of descriptor importance and image affinity are shown to provide a natural basis for intra- and inter-image prototype selection. Several variations of the proposed technique are empirically evaluated on the task of content-based image retrieval, demonstrating encouraging results.