Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Integrating salient colors with rotational invariant texture features for image representation in retrieval systems

Abstract: Content based image retrieval (CBIR) systems allow searching for visually similar images in large collections based on their contents. Visual contents are usually represented based on their properties like colors, shapes, and textures. In this paper, we propose to integrate two properties of images for constructing a discriminative and robust representation. Firstly, the input image is transformed into the HSV color space and then quantized into a limited number of representative colors. Secondly, texture features based on uniform patterns of rotated local binary patterns (RLBP) are extracted. The characteristics of color histogram populated from the quantized images and texture features are compared and analyzed for image representation. Consequently, the quantized color histogram and histogram of uniform patterns in RLBP are fused together to form a feature vector. Experimental evaluations with frequently used datasets show that the proposed method yields better results as compared to other state-of-the-art techniques.

Shape retrieval based on manifold learning by fusion of dissimilarity measures

Abstract: Content-based image retrieval (CBIR) is one of the most important research areas with applications in digital libraries, multimedia databases and the internet. Colour, texture, shape and spatial relations between objects are major features used in retrieval. Shape features are powerful clues for object identification. In this study, for improving retrieval accuracy, dissimilarities of contour and region-based shape retrieval methods were used. It is assumed that the fusion of two categories of shape description causes a considerable improvement in retrieval performance. The main goal in this study is to propose a new feature vector to coincide semantic and Euclidean distances. To accomplish this, the desired topological manifold was learnt by a distance-driven non-linear feature extraction method. The experiments showed that the geometrical distances between the samples on the manifold space are more related to their semantic distance. The proposed method was compared with other well-known approaches by MPEG-7 part B and Fish shape data sets. The results confirmed the effectiveness and validity of the proposed method.

Mean Normalized Retrieval Order (MNRO): a new content-based image retrieval performance measure

Abstract: The results of a content based image retrieval system can be evaluated by several performance measures, each one employing different evaluation criteria. Many of the methods used in the field of information retrieval have been adopted for use in image retrieval systems. This paper reviews the most widely used performance measures for retrieval evaluation with particular emphasis on the assumptions made during their design. More specifically, it focuses on the design principles of the commonly used Mean Average Precision (MAP) and Average Normalized Modified Retrieval Rank (ANMRR), pinpointing their limitations. It also proposes a new performance measure for image retrieval systems, the Mean Normalized Retrieval Order (MNRO), whose effectiveness is demonstrated through a wide range of experiments. Initial experiments were conducted on artificially produced query trials and evaluations. Experiments on a large database demonstrate the ability of MNRO to take into account the generality of the queries during the retrieval procedure. Furthermore, the results of a case study show that the proposed performance measure is closer to human evaluations, in comparison to MAP and ANMRR. Lastly, in order to encourage researchers and practitioners to use the proposed performance measure, we present the experimental results produced by a large number of state of the art descriptors applied on three well-known benchmarking databases.

Image database navigation: a globe-Al approach

Abstract: Image database visualisation and navigation tools become increasingly important as image collections keep ever growing. Demanded are easily navigable and intuitive ways of displaying and browsing image databases allowing the user to view images from a collection that facilitates finding images of interest. In this paper we introduce a way of viewing a complete collection of images by projecting them onto a spherical globe for colour-based image database navigation. Taking median hue and brightness of images, features that are useful also for image retrieval purposes, and using these as a set of co-ordinates which then determine the location on the surface of the globe where the image is projected. Navigation is performed by rotation (e.g. choosing a different hue range) and zooming into areas of interest.