Robust image retrieval using CCV, GCH, and MS-LBP descriptors

Content-Based Image Retrieval (CBIR) is a well-known research topic from the computer vision domain which helps retrieve similar images from a dataset as per the specified query image. The retrieval performance is inadequate for benchmark datasets viz., Corel-1k, Corel-5k, Corel-10k, and Ghim-10k. In this paper, we have encountered the problem of the low retrieval rates of the CBIR system and the high dimensionality of the feature vectors. We have proposed the hybrid framework consisting of three different feature descriptors for robust retrieval performance. We have propounded the use of modified Multi-Scale Local Binary Pattern (MS-LBP), Color Coherence Vector (CCV), and Global Color Histogram (GCH) for image retrieval. We have exerted the modified MS-LBP because of its ability to capture more texture detail than Local Binary Pattern (LBP) at multiple scales. This larger filter size of MS-LBP makes it less vulnerable to noise and illumination than the conventional LBP descriptor. The CCV captures color with location information well enough, but it’s vulnerable to the brightened images whereas, the GCH operator covers brightness (less sensitive to brightness than CCV), rotation, scale, translation, camera viewpoint invariant features, but lacks spatial information. The proposed framework improves the feature selection process by blending the strength of each of these descriptors. This paper also targets the high dimensionality of the feature vector of the MS-LBP and GCH descriptors by exerting Principal Component Analysis (PCA). Moreover, Linear Discriminant Analysis (LDA) selects robust and optimal features for retrieval. The proposed method is compared with state-of-the-art literature on four benchmark datasets in terms of Average Retrieval Precision (ARP), Average Retrieval Rate (ARR), and Retrieval Time (RT). Experimental results show that the proposed method excels the examined research practices.