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A Novel Active Learning Method in Relevance Feedback for Content Based Remote Sensing Image Retrieval
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A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval

Category : Image Processing


Sub Category : IMAGE RETRIEVAL


Project Code : IMP05


Project Abstract

Conventional relevance feedback (RF) schemes improve the performance of content-based image retrieval (CBIR) requiring the user to annotate a large number of images. To reduce the labeling effort of the user, this paper presents a novel active learning (AL) method to drive RF for retrieving remote sensing images from large archives in the framework of the support vector machine classifier. The proposed AL method is specifically designed for CBIR and defines an effective and as small as possible set of relevant and irrelevant images with regard to a general query image by jointly evaluating three criteria: 1) uncertainty; 2) diversity; and 3) density of images in the archive. The uncertainty and diversity criteria aim at selecting the most informative images in the archive, whereas the density criterion goal is to choose the images that are representative of the underlying distribution of data in the archive. The proposed AL method assesses jointly the three criteria based on two successive steps. In the first step, the most uncertain (i.e., ambiguous) images are selected from the archive on the basis of the margin sampling strategy. In the second step, the images that are both diverse (i.e., distant) to each other and associated to the high-density regions of the image feature space in the archive are chosen from the most uncertain images. This step is achieved by a novel clustering-based strategy. The proposed AL method for driving the RF contributes to mitigate problems of unbalanced and biased set of relevant and irrelevant images.


 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          The performance of tag matching-based retrieval approaches highly depends on the availability and the quality of manual tags. However, in practice, keywords/tags are expensive to obtain and often ambiguous.

         The semantic gap that occurred between the low-level features and the high-level semantic content and leads to Poor CBIR performance.

PROPOSED CONCEPT:

          Recent studies have shown that the content of the RS data is more relevant than manual tags. so we choose content of RS data instead of  manual tags.

          In order to confine the semantic gap, relevance feedback (RF) schemes have been designed to iteratively improve the performance of CBIR.

EXISTING  TECHNIQUE :

         SUPPORT VECTOR MACHINES (SVM)

PROPOSED ALGORITHM:

         TRIPLE CRITERIA ACTIVE LEARNING (TCAL)

TECHNIQUE DEFINITION:

          The Support Vector Machines (SVM) is an approximate implementation of the structural risk minimization (SRM) principle. The SVM can provide a good generalization performance on pattern classification problems without incorporating problem domain knowledge.

ALGORITHM DEFINITION:

         The proposed TCAL method is defined in the context of binary SVM classification and selects a batch S = {X1,X2, . . . ,Xh} of h images at each RF iteration that are as follows: 1) uncertain (i.e., ambiguous); 2) as more diverse as possible to each other; and 3) located in the highest density regions of the image feature space.

DRAWBACKS:

          The performance of tag matching-based retrieval approaches highly depends on the availability and the quality of manual tags. However, in practice, keywords/tags are expensive to obtain and often ambiguous.

          Images that fall into the high-density regions of the image feature (descriptor) space are crucial for CBIR problems particularly when a small number of initially annotated images are available.

ADVANTAGES:

          The annotation cost is reduced due to the avoidance of redundant images.

          Accurate retrieval accuracy can be obtained due to the improved class models estimated on a high-quality training set on the basis of the classification rule used from the considered classifier.

          The HI kernel has the advantage to be parameter-free.


 
 
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