Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Abstract

Content-Based Image Retrieval (CBIR) has received an extensive attention from researchers due to the rapid growing and widespread of image databases. Despite the massive research efforts consumed for CBIR, the completely satisfactory results have not yet been attained. In this article, we offer a new CBIR technique that relies on extracting Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) feature descriptors as well as the color features; color correlograms and Improved Color Coherence Vector (ICCV). These features are joined and used to build a multidimensional feature vector. Bag-of-Visual-Words (BoVW) technique is utilized to quantize the extracted feature vector. Then, a multiclass Support Vector Machine (SVM) is implemented to classify the query images. The performance of the presented retrieval framework is analyzed and scrutinized by comparing it with three alternative approaches. The first one is based on extracting SURF descriptors while the second one is based on extracting SURF descriptors, color correlograms and ICCV. The third approach, on the other hand, is based on extracting MSER, color correlograms and ICCV. All implemented schemes are tested on two benchmark datasets; Corel-1000 and COIL-100 datasets. The empirical results show that our suggested approach has a superior discriminative classification and retrieval performance with respect to other approaches. The proposed method achieves average precisions of 88 and 93% for the Corel-1000 and COIL-100 datasets, respectively. Moreover, the proposed system has shown a substantial advance in the retrieval precision when compared with other existing systems.

A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification

“…Data mining for image classification is an essential research area in computer science. It is a very effective and challenging task in several application domains, including medicine (Dash and Panda, 2016;Diz et al, 2016;Singh et al, 2016), remote sensing (Lu and Weng, 2007), facial micro expressions (Huang et al, 2012;2016), face recognition (Luo and Zhang, 2014) and etc.…”

“…In the third step, the frequency of each visual word in the image is computed. In view of that, BoVW scheme creates a histogram of visual features occurrences that represents an image (Liu et al, 2016;Elnemr, 2016). The obtained histograms are utilized to train an image category classifier.…”

“…In order to build the dictionary of visual words, feature extraction task is performed. In this work, we implemented the feature extraction approach proposed by (Elnemr, 2016). The algorithm is carried out by detecting SURF interesting points and MSER interesting regions.…”

“…In general, image classification is a twostep procedure; feature extraction followed by classifier implementation. In the feature extraction step, we applied the feature extraction algorithm presented in (Elnemr, 2016). This algorithm is based on extracting image keypoints and then mapping these keypoints into visual words.…”

“…This algorithm is based on extracting image keypoints and then mapping these keypoints into visual words. Recently, there is a tendency of applying image keypoints (interesting points) or keyregions (interesting regions) for retrieving and classifying images (Elnemr, 2016;Yang et al, 2007;Liu et al, 2016;Wang et al, 2016;Benkrama et al, 2013). Keypoints and keyregions contain valuable local information about an image that can be automatically detected by exploiting several detectors and characterized by many descriptors (Lowe, 2004;Krig, 2014).…”