Average Analysis Method in Selecting Haralick’s Texture Features on Color Co-occurrence Matrix for Texture Based Image Retrieval

다국어 초록 (Multilingual Abstract)

Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Work
• 3. Methodology
• 3.1 Generated Color Co-Occurrence Matrix (CCM)
• 3.2 Extraction the Haralick Textures on CCM
• 3.3 Feature Selection
• 3.4 Principal Component Analysis
• 3.5 Average Analysis
• 3.6 Similarity Measure and Performance Evaluation
• 4. Experiment Setup
• 5. Results and Discussion
• 6. Conclusion and Future Work
• References