Image Retrieval Process Based on Relevance Feedback and Ontology Using Decision Tree

다국어 초록 (Multilingual Abstract)

In this paper, another strategy for immediate features based image recovery is proposed. Image database is developed with low level texture features got from Gray Level Co- Occurrence Matrix (GLCM) and measurable techniques for Tamura. Semantic level inquiries from the user mapped to the low level peculiarities at recovery time to recover the required images. Images with more than one moderate features can be recovered by utilizing intersection of images recovered by each of the queried feature. Artificial Neural Network (ANN) is utilized as a part of the following steps in the wake of accepting user inputs. In spite of the fact that semantics are utilized as search key as a part of the beginning steps, low level features are utilized as a part of the ANN based searching in later steps. Back propagation Algorithm is utilized as a part of learning step. This ANN based relevance feedback technique enhances accuracy of immediate feature based image retrieval method. Decision tree (DT) can likewise be connected in relevance feedback stage. Decision tree is framed in training stage and images will be tested by of the decision tree. Relation storing ontology related information is utilized as a part of every phase of retrieval procedure to evacuate ambiguities identified with synonyms and hypernym-homonym sets.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Work
• 3. Proposed System
• 3.1. Low Level Texture Features’ Extraction and Storage
• 3.2. Image Retrieval by SQL
• 3.3. Relevance Feedback for Image Retrieval
• 3.4. Image Retrieval using Ontology Mapping
• 4. Results
• 5. Conclusion
• References