Graph Based Segmentation in Content Based Image Retrieval

Abstract

Problem statement: Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. To meet the requirement as a preprocessing step Graph based segmentation is used in Content Based Image Retrieval (CBIR). Approach: Graph based segmentation is has the ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions. After segmentation the features are extracted for the segmented images, texture features using wavelet transform and color features using histogram model and the segmented query image features are compared with the features of segmented data base images. The similarity measure used for texture features is Euclidean distance measure and for color features Quadratic distance approach. Results: The experimental results demonstrate about 12% improvement in the performance for color feature with segmentation. Conclusions/Recommendations: Along with this improvement Neural network learning can be embedded in this system to reduce the semantic gap.

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MST Segmentation for Content-Based Medical Image Retrieval

“…Image segmentation algorithms have been used as in CBIR systems [3][4][5]. Graph-based segmentation algorithms have been used in medical imaging application [6] [7], and in CBIR as a preprocessing step [8]. It has the ability to preserve detail in low-variability image regions while ignoring detail in highvariability regions.…”

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