International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
A Survey on Different Relevance Feedback Techniques in Content Based Image Retrieval Athira Mohanan1, Sabitha Raju2
PG student, Dept. Computer science engineering, VJCET, Vazhakulam, Kerala, India Assistant professor, Dept. Computer science engineering, VJCET, Vazhakulam, Kerala, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The conventional image retrieval methods like
Google, Bingo, Yahoo are based on the on textual annotation of images to access the large collection of relevant database images. Then Content Based Image Retrieval (CBIR) is a technique, which takes visual contents of image to retrieve relevant images from large databases. In Content Based Image Retrieval, there is a semantic gap between the low level features and high level semantic concepts. Different relevance feedback techniques bridge this semantic gap. In this paper analyse different subspace learning based relevance feedback algorithm to retrieve images. Key Words: Content based image retrieval, Semantics gap, Relevance feedback, Feature modification, Subspace learning
1. INTRODUCTION Content Based Image Retrieval (CBIR) has attracted much attention during the past decades. CBIR is an image retrieval techniques used to retrieve relevant images without using any image annotations. CBIR systems uses visual content of an image such as color, shape and texture features as image index [9]. The CBIR systems adopt the Euclidean distance metric in a high dimensional low level visual feature space to measure the similarity between the query image and the images in the database. But the Euclidean distance metric in a high-dimensional space is usually not very effective due to the gap between the low-level visual features and the high level semantic concepts [8]. Thus performance of CBIR system is poor due to the semantics gap between the input image and low level visual features [9]. The effect of semantics gap is avoided by using relevance feedback technique. Relevance feedback is a powerful tool and online learning to retrieve most relevant images. This strategy ask user to give some feedbacks on the results returned in the previous query round and come up with a better result based on these feedbacks. A variety of relevance feedback techniques designed to bride the semantics gap between low level visual features and high level semantic concept of each image [7]. The general process of Relevance Feedback is as follows: First user labels a number of relevant images as positive feedback and a number of irrelevant images as negative feedback from retrieved images. Then the CBIR system then refines its retrieval procedure based on these labeled samples. These processes carried out iteratively. RF techniques are classified into two categories: that is query movement and biased subspace learning. In this biased Š 2017, IRJET
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Impact Factor value: 5.181
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subspace learning, all positive samples are alike and each negative samples in negative in its own way [8]. 2. LITERATURE SURVEY In [1] Anelia Grigorova, Francesco G. B. De Natale, Charlie Dagli, Thomas S. Huang, Life Fellow, presents a feature adaptation techniques to retrieve more relevant images. It is an effective feature space dimension reduction according to user’s feedback, but also improves the image description during the retrieval process by introducing new significant features. FA-RF uses two iterative techniques to make use of the relevance information that is query refinement and feature re-weighting. For the adaptation of across RF uses the descriptions of both relevant and irrelevant image, as well as their number and proportions. The query image is located near to the boundary of the relevant cluster in the feature space then the system contains few relevant images. Thus the query refinement mechanism is useful to move the query towards the middle of the cluster of relevant images in the feature space. This FA-RF performs very well in terms of capability in identifying most important features and assigning them higher weights compared with classical feature selection algorithms. Also maintain compact image description. The main drawbacks are less efficient for large databases. There is also needs an efficient feature extraction algorithm. In [2] Mohammed Lamine Kherfi and Djemel Ziou proposed a new RF framework that combines the advantages of using both the positive example (PE) and the negative example (NE). This method learns image features and then applies the results to define similarity measures that correspond to the user judgement. The use of the NE allows images undesired by the user to be discarded, thereby improving retrieval accuracy. This method tries to learn the weights the user assigns to image features and then to apply the results obtained for retrieval purposes. It also reduces retrieval time. It clusters the query data into classes and model missing data, and support queries with multiple PE and/or NE classes. The main function of this method is that it assigns more importance to features with a high likelihood and those which distinguish well between PE classes and NE classes. The drawbacks are small sample problem. Also the use of PE is sufficient to obtain satisfactory results. In [3] Dacheng Tao, Xiaoou Tang, Xuelong Li and Xindong Wu, presents an Asymmetric Bagging and Random Subspace based Support Vector Machine (ABRS-SVM) to solve the problems of SVM in image retrieval and over fitting problem. ISO 9001:2008 Certified Journal
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