SRBIR: Semantic Region Based Image Retrieval by Extracting the Dominant Region and Semantic Learning

Problem statement: The Semantic Region Based Image Retrieval (SRBIR) system that automatically segments the dominant foreground region, consisting of the semantic concept of the image, such as elephants, roses and does the semantic learning, is proposed. Approach: The system segments an image into different regions and finds the dominant foreground region in it, which is the semantic concept of that image. Then it extracts the low-level features of that dominant foreground region. The Support Vector Machine-Binary Decision Tree (SVM-BDT) is used for semantic learning and it finds the semantic category of an image. The low level features of the dominant region of each category image are used to find the semantic template of that category. The SVM-BDT is constructed with the help of these semantic templates. The high level concept of the query image is obtained using this SVM-BDT. Similarity matching is done between the query image and the set of images belonging to the semantic category of the query image and the top images with least distances are retrieved. Results: Experiments were conducted using the COREL dataset consisting of 10,000 images and its subset with 1000 images of 10 different semantic categories. The obtained results demonstrate the effectiveness of the proposed framework, compared to those of the commonly used region based image retrieval approaches. Conclusion: Efficient image searching, browsing and retrieval are required by users from various domains, such as medicine, fashion, architecture, training and teaching. The proposed SRBIR system aims at retrieving images based on their semantic content by extracting the dominant foreground region in the image and learning its semantic concept with the help of the SVM-BDT. The proposed SRBIR system provides an efficient image search based on semantics, with high accuracy and less access time.