Image Retrieval With Relevance Feedback By Using Semisupervised Biased Maximum Margin Analysis

In past few years CBIR (content based image retrieval) system is most popular in retrieving the images. But it will not fill the gap between low-level visual features and high-level semantic concepts. To fill this gap relevance feedback was introduced as a powerful tool to improve the performance of the CBIR system. Various relevance feedback techniques are used to improve the performance like.query movement and reweighting algorithm, classification based algorithm, subspace learning algorithm.e. t.c. By these techniques it will need to perform more number of iterations. For this purpose Support Vector Machine (SVM) active learning algorithm is introduced in relevance feedback. But there are two drawbacks are there by using these SVM based RF techniques. First it will treat positive and negative feedbacks equally. Second it will not consider unlabeled samples. To overcome these two drawbacks BMMA and SemiBMMA are introduced in SVM based RF.BMMA is used to analyze the difference between positive and negative samples unequally. SemiBMMA is used for considering the unlabeled samples by introducing laplacian regularizer to the BMMA.In the CBIR system images are retrieved based on the computation of the similarity between the user’s query and images via a query by example (QBE) system. But the hidden problem is that the extracted visual features are too diverse to capture the concept of the user’s query. To solve these type of problems, in the QBE system an automatic method is proposed to estimate the semantic similarity between words or entities using web search engines. 1.INTRODUCTION During the past few years,Keyword-based image retrieval is used for retrieving the images.There are some drawbacks are there with these system,but one of the main drawback is”a real world image contain different concepts it is difficult to annotate such an image by small number of keywords”. Consider a case if user wants to retrieve an image ”world map”which contains country names and state names, in this case it is very difficult for user to 518 International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 8, August 2013 IJ E R T IJ E R T ISSN: 2278-0181 www.ijert. org IJERTV2IS80177 annotate names of countries which are present in the world map.To overcome those drawbacks content based image retrieval(CBIR) system was introduced.In the CBIR system an image is retrieved based based on low-level visual features(color,shape,texture.e.t.c),but it will not characterize the high-level semantic concepts.That means it will not provide what the human expect.To fill this semantic gap between low-level visual features and high-level semantic concepts relevance feedback(RF) was introduced as a powerful tool to improve the performance of the CBIR system.Relevance feedback provides interaction between human and computer by using feedback concept.There are two types of feedbacks are there.They are (1)Positive Feedback(2)Negative Feedback.If the retrieved image is similar to the target image then the user will labeled it as a relevant (or) positive.If the retrieved image is not similar to the target image then the user will labeled it as irrelevant(or)negative. Several relevance feedback techniques are used for retrieving relevant images.some of the techniques are Query movement and reweighting techniques,density estimation method,subspace learning technique,classification based algorithm.e. t.c. Among these techniques classification based RF is one of the most popular technique in CBIR system.But by using RF in CBIR system it will need to perform more iterations for retrieving more relevant images.To overcome this problem active learning algorithm is used.Several active learning algorithms are there but among these Support vector machine(SVM) active learning algorithm is used in relevance feedback.In this SVM based RF hyperplane is used to differentiate positive and negative feedbacks. 519 International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 8, August 2013 IJ E R T IJ E R T ISSN: 2278-0181 www.ijert. org IJERTV2IS80177 But by using SVM based RF in Content based image retrieval(CBIR) system there are two main drawbacks are there. (1)It will consider both positive and negative feedback classes while retrieving images (2)It will not consider unlabeled samples.To overcome these two drawbacks Biased maximum margin analysis(BMMA) and Semi BMMA are introduced for SVM based RF based on the Graph-embedding framework.BMMA is used for differentiating the positive and negative feedback samples.SemiBMMA is used for considering the unlabeled samples.To utilize the information of unlabeled samples in the databased Laplacian regularizer is introduced in to the BMMA which will lead to SemiBMMA. In general, the purpose of CBIR is to retrieve relevant images based on low-level visual features such as color, texture, and shape. In These conventional approaches images are retrieved based on the computation of the similarity between the user’s query and images via a query by example (QBE) system. In spite of the power of search strategies, it is very difficult to improve the retrieval quality of CBIR within only one query process. The main hidden problem is that the extracted visual features are widely varied to capture the concept of the user’s query. To solve these problems, in the QBE system an automatic method is proposed to estimate the semantic similarity between words or entities using web search engines. In the existing system SVM (Support Vector Machine) Technique was used. Where the images are tagged and trained in the training module, and then the results can be retrieved . we propose an enhanced method of CBIR with considering the ambiguity between words and solves the ambiguity problem by using Web Search Engines. 2.Related Previous Work 2.1Support Vector Machine In CBIR system it is possible to capture only one aspect of image property. To overcome this problem Relevance Feedback was introduced. It is used to establish the link between high-level concepts and low-level features. By using the following three steps 520 International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 8, August 2013 IJ E R T IJ E R T ISSN: 2278-0181 www.ijert. org IJERTV2IS80177 it will establish the link between humans and computers. Step1:For a given query first retrieve a list of ranked images according to predefined similarity metrics. Step2:The user marks the retrieved images as relevant to the query or not relevant. Step3:The system will refine the retrieval results based on the feedback and present new list of images to the user. Some relevance feedback techniques are used for performing these tasks.But by using these techniques it will need to perform lot of iterations.To overcome this problem support vector machine is used.support vector machine is an active learning algorithm.By using this algorithm images can be retrieved very effectively. Support vector Active Learning Algorithm