A hybrid multi-objective optimization algorithm for content based image retrieval

Relevance feedback methods in CBIR (Content Based Image Retrieval) iteratively use relevance information from the user to search the space for other relevant samples. As several regions of interest may be scattered through the space, an effective search algorithm should balance the exploration of the space to find new potential regions of interest and the exploitation of areas around samples which are known relevant. However, many algorithms concentrate the search on areas which are close to the images that the user has marked as relevant, according to a distance function in the (possibly deformed) multidimensional feature space. This maximizes the number of relevant images retrieved at the first iterations, but limits the discovery of new regions of interest and may leave unexplored a large section of the space. In this paper, we propose a novel hybrid approach that uses a scattered search algorithm based on NSGA II (Non-dominated Sorting Genetic Algorithm) only at the first iteration of the relevance feedback process, and then switches to an exploitation algorithm. The combined approach has been tested on three databases and in combination with several other methods. When the hybrid method does not produce better results from the first iteration, it soon catches up and improves both precision and recall.     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

A multi-agent platform for content-based image retrieval

Efficient and possibly intelligent image retrieval is an important task, often required in many fields of human activity. While traditional database indexing techniques exhibit a remarkable performance in textual information retrieval current research in content-based image retrieval is focused on developing novel techniques that are biologically motivated and efficient. It is well known that humans have a remarkable ability to process visual information and to handle the volume and complexity of such information quite efficiently. In this paper, we present a content-based image retrieval platform that is based on a multi-agent architecture. Each agent is responsible for assessing the similarity of the query image to each candidate image contained in a collection based on a specific primitive feature and a corresponding similarity criterion. The outputs of various agents are integrated using one of several voting schemes supported by the system. The system’s performance has been evaluated using various collections of images, as well as images obtained in specific application domains such as medical imaging. The initial evaluation has yielded very promising results.

基于内容图像检索中的相关反馈技术研究*

简要介绍了基于内容的图像检索,讨论了相关反馈的基本思想及其交互过程,重点对现有的相关反馈算法进行了分类总结和分析,并展望了今后相关反馈技术的发展趋势。     

Visual guided navigation for image retrieval

In this work, we are interested in technologies that will allow users to actively browse and navigate large image databases and to retrieve images through interactive fast browsing and navigation. The development of a browsing/navigation-based image retrieval system has at least two challenges. The first is that the system's graphical user interface (GUI) should intuitively reflect the distribution of the images in the database in order to provide the users with a mental picture of the database content and a sense of orientation during the course of browsing/navigation. The second is that it has to be fast and responsive, and be able to respond to users actions at an interactive speed in order to engage the users. We have developed a method that attempts to address these challenges of a browsing/navigation based image retrieval systems. The unique feature of the method is that we take an integrated approach to the design of the browsing/navigation GUI and the indexing and organization of the images in the database. The GUI is tightly coupled with the algorithms that run in the background. The visual cues of the GUI are logically linked with various parts of the repository (image clusters of various particular visual themes) thus providing intuitive correspondences between the GUI and the database contents. In the backend, the images are organized into a binary tree data structure using a sequential maximal information coding algorithm and each image is indexed by an n-bit binary index thus making response to users’ action very fast. We present experimental results to demonstrate the usefulness of our method both as a pre-filtering tool and for developing browsing/navigation systems for fast image retrieval from large image databases.     

An improved distance-based relevance feedback strategy for image retrieval

Most CBIR (content based image retrieval) systems use relevance feedback as a mechanism to improve retrieval results. NN (nearest neighbor) approaches provide an efficient method to compute relevance scores, by using estimated densities of relevant and non-relevant samples in a particular feature space. In this paper, particularities of the CBIR problem are exploited to propose an improved relevance feedback algorithm based on the NN approach. The resulting method has been tested in a number of different situations and compared to the standard NN approach and other existing relevance feedback mechanisms. Experimental results evidence significant improvements in most cases.     

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.     

NMF和Isomap相结合的图像检索新方法*

非负矩阵分解(NMF)能够提取图像的局部特征,是一种基于局部的数据挖掘方法,在一定程度上勾勒出了相关图像在基矩阵所代表空间上的分布,然而NMF并未考虑数据的内在几何结构。提出了一种新颖的基于非负矩阵分解和非线性降维方法Isomap相结合的新方法,全局的非线性降维方法Isomap能发现数据的内在结构和相关性,使高维数据在低维空间变得可视化。将本算法应用于图像检索,实验表明,该方法能够更加准确的获取信息,提高检索的准确性。     

Towards intelligent image retrieval

Research into techniques for the retrieval of images by semantic content is still in its infancy. This paper reviews recent trends in the field, distinguishing four separate lines of activity: automatic scene analysis, model-based and statistical approaches to object classification, and adaptive learning from user feedback. It compares the strengths and weaknesses of model-based and adaptive techniques, and argues that further advances in the field are likely to involve the increasing use of techniques from the field of artificial intelligence.     

Image retrieval using nonlinear manifold embedding

The huge number of images on the Web gives rise to the content-based image retrieval (CBIR) as the text-based search techniques cannot cater to the needs of precisely retrieving Web images. However, CBIR comes with a fundamental flaw: the semantic gap between high-level semantic concepts and low-level visual features. Consequently, relevance feedback is introduced into CBIR to learn the subjective needs of users. However, in practical applications the limited number of user feedbacks is usually overwhelmed by the large number of dimensionalities of the visual feature space. To address this issue, a novel semi-supervised learning method for dimensionality reduction, namely kernel maximum margin projection (KMMP) is proposed in this paper based on our previous work of maximum margin projection (MMP). Unlike traditional dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA), which only see the global Euclidean structure, KMMP is designed for discovering the local manifold structure. After projecting the images into a lower dimensional subspace, KMMP significantly improves the performance of image retrieval. The experimental results on Corel image database demonstrate the effectiveness of our proposed nonlinear algorithm.