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.

A Survey on Current Content based Image Retrieval Methods

Abstract: 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.

Content-based shape retrieval using different shape descriptors: a comparative study

Abstract: Shape representation is a fundamental issue in the newly emerging multimedia applications. In the content based image retrieval (CBIR), shape is an important low level image feature. Many shape representations have been proposed. However, for CBIR, a shape representation should satisfy several properties such as affine invariance, robustness, compactness, low computation complexity and perceptual similarity measurement. Against these properties, in this paper we attempt to study and compare several shape descriptors which have been widely adopted for CBIR, they are: Fourier descriptors (FD), curvature scale space (CSS) descriptors (CSSD), Zernike moment descriptors (ZMD) and grid descriptors (GD). The strengths and limitations of these methods are analyzed and clarified. Retrieval results are given to show the comparison.

A unified log-based relevance feedback scheme for image retrieval

Abstract: Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

Abstract: Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate.