Enhancing the retrieval performance by combing the texture and edge features
This work addresses image retrieval performance for applications like databases or search, but it is incremental as it builds on known techniques like LBP and geometric moments.
The paper tackles content-based image retrieval by proposing an algorithm that combines geometric moments and local binary patterns (LBP) to generate feature vectors, resulting in significant improvement over existing methods like LBP and other transform domain techniques.
In this paper, anew algorithm which is based on geometrical moments and local binary patterns (LBP) for content based image retrieval (CBIR) is proposed. In geometrical moments, each vector is compared with the all other vectors for edge map generation. The same concept is utilized at LBP calculation which is generating nine LBP patterns from a given 3x3 pattern. Finally, nine LBP histograms are calculated which are used as a feature vector for image retrieval. Moments are important features used in recognition of different types of images. Two experiments have been carried out for proving the worth of our algorithm. The results after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.