Research On Feature Representation And Retrieval Strategy For Content-based Image Retrieval

Recently, with the rapid development of Internet and computer technology, the societyhas entered the network data era marked by “big data”. Recently, international top journalssuch as Nature and Science have been published special issues to discuss the research oflarge data. The study of network data plays an important role in maintaining social stability,promoting social development, upgrading industry competition ability, emerging strategicindustries and the growth of scientific research methodology.As an important part of network data, image is one kinds of multimedia information,which contains abundant information content. With the growing popularity of Internet andthe continuous development of network technology, the image is more and more becomingthe mainstream of network data. How to dig the large information in the image, and how torealize the effective organization, analysis and management for image data have become animportant development direction and research hot spot in the field of information processingin the era of network data.Network image data has great amount of data information, higher dimension andresolution, unstructured data morphology, and the diversity, vagueness, uncertainty toexplain. These characteristics make the related research achievements difficult to be directlycopied to the study of network image data. How to reasonable group, express, store, queryand retrieve these huge amounts of image data is the problem we face to study and solvecurrently as well as a significant challenge.Therefore, how to establish efficient image retrieval model and method can cross themultidisciplinary, develop the theories and methods of digital image processing, patternrecognition, statistical learning and machine vision synthetically, combine with traditionaldatabase technology, and can set up the effective relation with high-level semanticinformation according to the low-level visual attributes of images, provide the image retrieval model and method with good performance, and retrieve the required andsatisfactory image for user, which is of important theoretical significance and practical value.Therefore, content-based image retrieval technology has become the research focus andtrend to solve the problem.The recent retrieval model and method is very difficult to meet the needs for people,which reason is that the most expression and description for image are based on thelow-level physical attributes, which also has a huge gap with the user’s intent named as thehigh-level semantic information (such as the description of image topic type, events, emotionexpression, etc). Therefore, on the one hand, we need to study more efficient description andexpression of image model and method, on the other hand is to make full use of the userinformation to set up a bridge between the low-level visual features and high-level semanticconcepts.In this paper, main work is as follows:1. Image retrieval based on improved random walkThe classic image retrieval based on random walk is a relatively perfect retrieval modeland method. However, in the process of random walk, they did not consider the imagecomponent feature image as well as the importance of the spatial relationship betweenfactors, which often encounter problems in image representation and retrieval efficiency.This paper proposes two solving strategy:Firstly, based on the importance of image features in retrieval, we take into account toadopt feature selection methods and weight for image component to characterize theimportance of the each feature. The Laplacian score has good local keeping ability, thus, it isutilized for feature weight.Secondly, each K neighbor embodies the spatial structure relations between images.Therefore, K neighbor selection method is combined with random walk. In the process ofrelevance feedback, looking for all K neighbor of all relevant and irrelevant images improvesretrieval efficiency greatly which can ensure the solution more comprehensive and better.Extensive experiments on classic database demonstrate that the proposed method is superiorto some well-known methods. At last, we improve the random walks-based image retrieval in two ways, i.e., addingfeature re-weighting and K nearest neighbor method into random walks-based imageretrieval simultaneously.The retrieval result of our method is superior to other two compared methods. Using thesame image feature, our method outperforms other two compared methods significantly ondifferent datasets. In the terms of precision and recall, our method performs better than othertwo compared methods. At the running time, our method is almost the same with classicalrandom walks-based image retrieval method.2. Dynamic manifold feature updating based feedback retrievalCompared with traditional linear dimensionality reduction methods, manifold learningbased method is characterized by assuming that samples distributing in high-dimensionalspace are in or approximately in a nonlinear manifold. The aim of manifold learning is tofind the nonlinear manifold structure in dataset and maintain the structural information asmuch as possible while reducing the dimensionality.We propose a dynamic manifold feature updating based feedback retrieval methodbased on Maximum Margin Criterion (MMC). This method finds the optimal linear subspacebased on maximizing the margin between classes and creates dynamically a manifold spaceas query by using the feedback information of the user. Therefore, MMC can improve thecomputational efficiency, while avoiding the small sample size problem.Firstly, reduce the dimensionality of original feature space through MMC, by makinguse of the relevant images selected by user, and obtain the transformed matrix.Secondly, apply the transformed matrix to original features. According to the updatedfeatures and labeled samples by user, compute the scores of all samples by random walksalgorithm.Lastly, display the K images with highest scores to the user, until the condition issatisfied.3. Content and mutual information descriptor based image retrievalIn this paper, a content based image representation method for image retrieval is proposed, called mutual information descriptor. The extraction and representation of mutualinformation descriptor is achieved under the guideless of visual cognitive mechanism. Thismethod extracts features in following the human visual cognitive mechanism and thestructure of human eyes, and fuses features by simulating the process of informationtransmission in neural system. The features are extracted in the pre-attentive stage by usingmutual information consistency descriptor, and are represented in the attentive stage.Mutual information consistency descriptor constructs orientation-sensitive andcolor-sensitive maps, for the characteristic that cone and rod cells are more sensitive to colorand orientation. So, the mutual information consistency descriptor compliances with thecharacteristics of human retinal cells in acquiring features. The representation of mutualinformation consistency descriptor is implemented by simulating the process of informationtransmission in neural system. In the fusion step, features extracted by retinal cells areweighted with constraint by simulating the surrounding environment using machine learning.Finally, reconstruct the image in "brain" by the feature vector. Therefore, the mutualinformation consistency descriptor includes color, shape, texture and the distribution offeatures, and has ability of spatial localization to some extent. At the same time, the mutualinformation consistency descriptor has low dimensionality, which reduces the timecomplexity and space complexity greatly.Experimental results demonstrate that, under the same experimental conditions, mutualinformation consistency descriptor has higher retrieval ability compared with edgeorientation histogram and micro-structure descriptor, and is invariant to translation and affinetransformation, when applying to image retrieval. This is because that the mutualinformation consistency descriptor can extract simultaneously the properties of color, shape,texture and the distribution of features, and has ability of spatial localization to some extent.Therefore, the proposed method can retrieval images accurately and comprehensively.