New strategy for CBIR by combining low‐level visual features with a colour descriptor

IET Image ProcessingVolume 13, Issue 7 p. 1191-1200 Research ArticleFree Access New strategy for CBIR by combining low-level visual features with a colour descriptor Salahuddin Unar, Salahuddin Unar School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024 People's Republic of ChinaSearch for more papers by this authorXingyuan Wang, Corresponding Author Xingyuan Wang wangxy@dlut.edu.cn School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024 People's Republic of China School of Information Science and Technology, Dalian Maritime University, Dalian, 116026 People's Republic of ChinaSearch for more papers by this authorChunpeng Wang, Chunpeng Wang School of Information, Qilu University of Technology, Shandong, People's Republic of ChinaSearch for more papers by this authorMingxu Wang, Mingxu Wang School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024 People's Republic of ChinaSearch for more papers by this author Salahuddin Unar, Salahuddin Unar School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024 People's Republic of ChinaSearch for more papers by this authorXingyuan Wang, Corresponding Author Xingyuan Wang wangxy@dlut.edu.cn School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024 People's Republic of China School of Information Science and Technology, Dalian Maritime University, Dalian, 116026 People's Republic of ChinaSearch for more papers by this authorChunpeng Wang, Chunpeng Wang School of Information, Qilu University of Technology, Shandong, People's Republic of ChinaSearch for more papers by this authorMingxu Wang, Mingxu Wang School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024 People's Republic of ChinaSearch for more papers by this author First published: 07 May 2019 https://doi.org/10.1049/iet-ipr.2019.0098Citations: 8AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract In computer vision, the analysis of image contents plays a significant role to perform intelligent tasks such as object recognition and image retrieval. These contents can be low-level visual features or colour information within an image. For content-based image retrieval (CBIR), several methods have been proposed that focus on either low-level visual features extraction or the colour information, and very few works can be seen that retrieve the images by fusing both types of contents. Consequently, this work addresses the problem of combining low-level visual features with colour information that helps to improve the retrieval accuracy of CBIR. The proposed strategy extracts the low-level visual salient features with features from accelerated segment test feature descriptor and quantises the salient keypoints into a feature vector. The colour information of the image is extracted and segmented with non-linear L*a*b* colour space and quantised into a feature vector. The similarity for both the feature vectors including visual and colour features is computed and combined together. The top-rank images are retrieved for the obtained feature vector using the distance metric. The experimental results on two standard benchmark datasets show the improved efficiency and 85% accuracy of the proposed strategy over state-of-the-art methods. 1 Introduction The rapid development of various digital devices has created countless opportunities to capture, share, and retrieve digital images from huge repositories. In earlier days, retrieving the images was a simple and easy task due to the limited amount of images in repositories. Day by day, it became a more challenging task due to the increasing variety and complexity of images, as well as a vast amount of images. New methods and techniques have been developed to retrieve the images according to the desire of users, and image retrieval remains a hot topic in multimedia and computer vision communities [1-3]. Image retrieval methods can be categorised into two types: text-based image retrieval and content-based image retrieval (CBIR). The former method was used in earlier years when the images in databases were limited in quantity. The method uses the metadata of the image specified with the description and tags [4, 5]. However, such methods are not practical for today's huge repositories, and an excessive amount of human labor are required for annotating the images manually. Later on, the latter method was introduced that realises the user's desire and retrieve the images accordingly [6-8]. CBIR is a system for retrieving the images similar to the user-provided query image. The retrieved images can be similar in the sense of texture, shape, or combination of colours. CBIR architecture generally comprises three distinct steps that include extracting and selecting features, finding the similarity, and indexing and retrieving the top similar images. Several state-of-the-art studies have proposed distinctive techniques for these three steps [9-11]. Feature extraction and selection is considered to be the first and major step in which an image is analysed in whole or in parts. Many authors proposed feature descriptors that capture colour, texture, and shape of an object within the image [12-14]. The similarity measure is considered to be the second step that reduces the semantic gap between the images. It is computed by measuring the distance between the image descriptors such as Euclidean or Manhattan distances. Indexing and retrieving are considered to be the third step that helps to rank the images according to their similarity score and retrieve the images to the user. The state-of-the-art methods widely used feature descriptors to extract the visual characteristics of the image [15, 16]. There are two types of feature descriptors: global descriptors and local descriptors. Global descriptors may analyse the whole image and may consider colour, texture, and shape of an image. Local descriptors may analyse and extracts the specific objects within an image. Since the last two decades, local descriptors are extensively used for retrieving images such as scale-invariant feature transform (SIFT), speed-up robust feature (SURF), and histogram of oriented gradient (HoG). Feng et al. [10] proposed a global correlation descriptor that extracts colour and textural features. The descriptor helps to precise the colour and texture equally. Liu et al. [17] proposed a retrieval method by fusing colour information feature with local binary pattern features since the local binary pattern is not good at capturing the colour information. In order to achieve good accuracy, Wang et al. [18] proposed a method based on small separable vocabularies that are created from a large vocabulary. They employed sparse representation to retrieve the images from the small vocabularies. Husain and Bober [19] presented discriminative global descriptor named the Robust Visual Descriptor with whitening that is capable of multiple assignments, directional accumulation, and whitening of remaining vectors. A convolutional neural network-based approach is used that outperforms global descriptors with a sum-pooling approach. Liu and Yang [20] proposed a feature representation method based on colour histogram difference to describe the image features. They combined colour information with orientation in a unified framework for considering spatial layouts of both. Their method is good at discriminating colour, shape, and texture. Wang and Wang [21] proposed a novel structure element descriptor to extract colour and texture features. The texture descriptor is comprised of five structure elements based on five orientations. Zhang et al. [6] proposed image retrieval approach based on extended salient region oriented. They used an image representation model by considering salient objects within an image and background regions of an image. Liu et al. [22] proposed a visual attention model that extracts salient objects and retrieves the images based on salient objects. They used edge information with colour to detect the saliency regions. Due to an increasing demand for multi-tasking and feature complexity, some recent studies detected the textual features from the textual images and fused with the visual features to retrieve the similar images [23, 24]. Firstly, they detect and localise the text from the textual images and then the confidence score is assigned on the basis of correctly recognised text [25-27]. Despite extensive efforts have been made for CBIR to obtain better accuracy, there is still great need to retrieve the images with user perception. Consequently, we propose a novel strategy that combines visual salient features with colour information. In this paper, we introduce a novel strategy for CBIR based on salient features and colour spatial information. A feature descriptor exploits to locate the salient keypoints of an image and quantised into a feature vector. A colour space model exploits the colour information and segments the colours. These colours are quantised into a feature vector. The similarity of both the feature vectors (i.e. keypoints and colour) is computed and combined. The top-rank images are retrieved on the final computed similarity metric. The main contributions of our proposed strategy are as follows. A fast and robust CBIR approach is introduced that combines visual features and colour information. The proposed approach is innovative in dealing with highly complexed images. The computation of the proposed approach is efficient and accurate for real-time applications. The approach proposes an easy-to-use framework that is still based on distance metric and independent of a machine learning approach and that takes a lot of training efforts and computation time. The rest of the paper is organised as follows. The proposed strategy is defined and described briefly in Section 2 along with similarity measures. In Section 3, the experiments are evaluated for the proposed strategy on two datasets. Section 4 concludes the proposed work and defines future directions. 2 Proposed strategy In this section, we propose a novel strategy to fuse the salient visual features with the colour information. Firstly, the salient keypoints are located within an image with a feature descriptor. To create visual words' vocabulary, a k-means clustering approach is used that categorises the detected salient keypoints into k categories. A feature vector is created for the query and the database image. Secondly, the colour distribution is achieved and segmented using a colour space mode. A feature vector is created that contains the colour information. Next, the similarity is measured for both the feature vectors, and top-rank images are retrieved on the fused similarity measure. The schematic diagram of the proposed strategy is shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Schematic diagram of proposed strategy 2.1 Feature descriptor Retrieving the images based on visual characteristics requires a high level of semantic information. Several studies have been proposed to increase the retrieval accuracy of CBIR by extracting the low-level features such as texture, colour, and shape. These studies extensively used local feature descriptors such as SIFT [28], SURF [29], and local binary pattern (LBP) for extracting the keypoints of an image. However, these feature descriptors have some serious limitation such as computation time, scaling, and rotation. A comparative analysis of prior works that used low-level image features is given in Table 1. Table 1. Comparative analysis of art methods for low-level visual features Method Features Advantages Limitations [17] LBP (local binary pattern) robust with greyscale images simple computation noise-sensitive [30] HOG (histogram of oriented gradient) normalisation of local contrast independent of edge positions multiple bounding boxes for each object [31] SIFT (scale-invariant feature transform) invariant to scale and orientation affine distortion and illumination change high dimension matching fixed size matching vectors [32] SURF (speeded up robust features) detection with Hessian matrix -reduced computational cost poor keypoints localisation poor performance for rotation [33] GIST low dimension independent of pre-processing step less invariant To overcome these limitations, the proposed strategy employs FAST (features from accelerated segment test) feature descriptor that computes the salient keypoints efficiently and robustly [34]. It is an efficient and effective method to determine reasonable keypoints in an image. It assumes one parameter that is the centre of an image by computing the intensity threshold between the centre pixels and the circular ring pixels. Consider a circle of 16 pixels around the interested candidate p. To obtain a desirable target of N keypoints, we set the threshold t < N to obtain more keypoints. The candidate pixel p is considered to be a corner if a set of n contiguous pixels exists in the circle that appears brighter than the intensity of the candidate pixel or appears darker than . A high-speed test is attained that observe 1, 9, 5, and 13 pixels to discard uninterested keypoints. The test proceeds by examining pixels 1 and 9 at first place; either they are very bright or very dark; therefore, pixel p cannot be a keypoint. It continues to check pixels 5 and 13 for their being very bright or very dark. The pixel p can be a keypoint if three of the examined pixels are brighter than or darker than ; otherwise, pixel p cannot be a keypoint. Afterwards, all 16 pixel circles are extracted from the database images for keypoints of n. Given a pixel at each location, comparative to p that is acknowledged by may have one of the three conditions given as (1)If pixel x is completely darker than the centre pixel with threshold t, then all the keypoints belong to the class. If pixel x is completely brighter than the centre pixel with threshold t, then all the keypoints belong to the class. If pixel x remains the same as the centre pixel with threshold t, then all the points belong to the class. Consider P as the set of pixels from database images, x segments P into three categories, that is, , , and given as (2) (3) (4)In order to achieve maximum features, a decision-tree classifier is used. Consider a Boolean variable which is assumed to be true if pixel p is a keypoint and false otherwise. The entropy of K is measured for considering random keypoints as (5)where represents the number of true keypoints and represents the number of false keypoints. The attained keypoints given by (6)Here, segments in and segments into and segments into . The process stops when the entropy becomes zero which shows that all p values are processed with a condition either keypoints or non-keypoints. The detected keypoints are shown in Fig. 2. Fig. 2Open in figure viewerPowerPoint Detected salient keypoints. (a) Original query image, (b) Located keypoints in query image In Fig. 2, several adjacent keypoints are detected that may increase the computation time. We use non-maximum suppression to cope with the problem. A score function V is computed for all localised keypoints. The score function V is the total sum of differences among p and 16 pixels around the circle. Two adjacent keypoints are considered by computing their V score. Finally, a pixel with a lower V score is discarded. The final located keypoints after non-maximum suppression are shown in Fig. 3. Fig. 3Open in figure viewerPowerPoint Detected keypoints after non-max suppression (red are actual keypoints) (green are non-max supressed keypoints) (a) Query image, (b) Database image 2.2 Feature quantisation The keypoints detected in Section 2.1 are quantised into meaningful clusters in order to construct visual words vocabulary for image retrieval. We use k-means clustering approach for faster optimisation that clusters the detected keypoints into K number of categories. The approach helps process the keypoints by performing the iteration surrounding the cluster centres, and the process continues until k categories are computed. Consider the probability distribution of detected keypoints that belongs to different categories given as (7)The entropy P for the distributed probability is given as (8) (9)where represents the number of keypoints that belongs to the category j, and represents the sum of all keypoints. The entropy for each cluster after partitioning the keypoints into K clusters can be given as (10)where represents the ratio between the number of keypoints of the category in the cluster to the entire number of keypoints in the cluster ; we have now (11)The detected keypoints in a query image are denoted as where . The vocabulary for keypoints can be given as (12)To represent the linear form for multiple words, we have (13)where represents the -norm and is a lenience error. The non-zero values of the row in represents the frequency of the visual word. The histogram of frequency for each visual word is given as (14)For -norm optimisation, we have (15)where represents the -norm. The matched keypoints are shown in Fig. 4 between the query image and the database image. Fig. 4Open in figure viewerPowerPoint Salient keypoints matching between query image and database image 2.3 Colour descriptor Colour is a widely used low-level visual feature for recognising the objects in CBIR. Several colour descriptors are proposed in art methods that are extensively used in CBIR applications such as the colour difference histogram and colour correlogram [17, 20, 35]. The colour descriptor extracts the colour information of an image and distributes it in individual colour that represents the brightness of each pixel. Specifically, it represents a set of features that individually identifies the colour information [36]. Colour can be categorised into two types: linear colour and non-linear colour. The linear colour class includes RGB, XYZ, YUV, CMY, and YIQ colour models that consist of basic colour information. Such models represent the colour distribution in terms of a weighted sum of intensities containing basic colours only. On the other hand, a non-linear colour class includes L*a*b*, HSV, Nrgb, L*u*v*, and Nxyz colour models that consists of extensive colour information compared with the linear colour class. Such models represent rich colour distributions that help to understand by humans and can be best appropriate for computer vision tasks that need a better understanding of colours. In the proposed strategy, we extract the colour distribution by using the L*a*b* colour model. In this model, L* represents the luminosity layer, a* represents the chromaticity layer between the red and green axes, and b* represents the chromaticity layer between the blue and yellow axes. Specifically, a* and b* layers contain the information of colour distribution. L*, a*, and b* layers are achieved by mapping XYZ coordinates in a non-linear form. Consider the three unit vectors , , and in L*, a*, and b* axes, respectively, for a colour image , given as (16) (17)The dot products for the defined vectors are given as (18) (19) (20)The orientation for a maximum frequency of change with respect to is given as (21)The change of frequency for orientation is given as (22) (23) (24)The maximum and minimum gradients can be given as (25) (26)The orientation of for the gradient values can be given as (27)The projection of is obtained with the intervals of and edge orientations are computed for each pixel. The achieved segmented colours for different images are shown in Fig. 5. Fig. 5Open in figure viewerPowerPoint Different images segmented into different colours (a) Original images, (b–d) Segmented colours 2.4 Colour quantisation Quantisation is the process of reducing the colour distribution within an image. The segmentation achieved in Section 2.3 is quantised into 18 bins. The edge orientations are quantised in all possible directions between and with the interval of . The proposed strategy uniformly quantises L*, a*, and b* channels into 90 bins. The L* layer represents the brightness of colour and it ranges from 0 to 100, where 0 is for fully darker and 100 fully brighter. The a* layer represents colour opponent with red at positives and green at negatives, and its range is usually between −100 and 100 or −128 and 127. The b* layer represents the colour opponent with yellow at positives and blue at negatives, and its range is the same as that of the a* layer. More formally, we have (28)Here, L* is quantised into 10 bins, and a* and b* into 3 bins uniformly. The scatterplot of distributed pixels for different images (their original images are given in Fig. 5) with respect to a* and b* colour layers is shown in Fig. 6. Fig. 6Open in figure viewerPowerPoint Scatterplot of distributed pixels for different images (given in Fig. 5) w.r.t a*b* channels (a–f) With respect to Original query images given in Fig. 5 2.5 Similarity measure The visual salient features and colour information are extracted and represented through the histograms. These features are arranged in multi-dimensional vectors; thus, the similarity between them can be measured with some distance measurement such as the Euclidean distance. Given the feature vector for the detected salient keypoints and the feature vector for the colour descriptor the feature vector for the query image V is given as (29)and feature vector for the database image is given as (30)The similarity measure for the query image and the database image can be given as (31) (32) (33)where and are similarity weights for each feature vector and the total weight is given by . 3 Experimental results and discussion This section briefly discusses the experimental results for the proposed strategy. The experimental datasets and evaluation measure are given as follows. 3.1 Datasets We evaluated the proposed strategy on two standard benchmark datasets that are extensively used in art methods. Corel-1k. The dataset [37] comprises 1000 images that are divided into 10 different groups. The dimensionality for each image is either or . Oxford Flowers. This dataset [38] comprises 1360 images that are divided into 17 different groups. Each group contains 80 relevant images. The dimensionality for each image varies from to . 3.2 Retrieval performance accuracy For evaluating the retrieval performance of the proposed strategy, we employ the commonly used precision-recall ratio. Precision P is the ratio between the total numbers of the retrieved relevant images to the total number of the retrieved images. Recall that R is the ratio between the total numbers of the retrieved relevant images to the total number of the relevant images in the database. More specifically, we have (34) (35)The average precision value of each query is then averaged for all queries. The mean average precision (mAP) for the query image Q can be given as (36)where k is the number of top retrieved images. 3.3 Evaluation results This section evaluates the experimental results for the datasets defined in Section 3.1. Initially, the low-level salient keypoints are detected and quantised into a feature vector. Next, the colour features are detected and segmented using the L*a*b* colour space model and quantised into a feature vector. The similarity for both the feature vectors is computed and combined as defined in Section 2.5. Finally, the top k images are retrieved using the performance measures defined in Section 3.2. 3.3.1 Analysis of evaluation for Corel dataset In the first experiment, we evaluated the proposed strategy on the Corel-1k dataset. For this purpose, 100 images are randomly selected from each category and used as a query image. The sample images for different categories are shown in Fig. 7. Fig. 7Open in figure viewerPowerPoint Sample images from different categories of Corel-1k dataset In this experiment, we compared the retrieval results with well-known state-of-the-art methods [30, 39-41]. When the user provides a query image, the system retrieves the top k rank similar images by computing visual salient features and colour information. For each category, the numerical results for average precision rates are reported in Table 2 and average recall rates are reported in Table 3. Table 2. Average precision comparison of different classes in the Corel dataset Class/method Our method Ref. [39] Ref. [40] Ref. [30] Ref. [41] African 0.694 0.726 0.728 0.730 0.57 beach 0.714 0.593 0.694 0.612 0.58 building 0.847 0.587 0.662 0.597 0.43 bus 0.958 0.891 0.971 0.890 0.93 dinosaur 0.948 0.993 0.100 0.990 0.98 elephant 0.846 0.702 0.808 0.713 0.58 flower 0.969 0.928 0.946 0.916 0.83 horse 0.937 0.856 0.908 0.862 0.68 mountain 0.822 0.562 0.762 0.573 0.46 food 0.851 0.772 0.860 0.781 0.53 average 0.858 0.761 0.834 0.766 0.657 Table 3. Average recall comparison of different classes in the Corel dataset Class/method Our method Ref. [39] Ref. [40] Ref. [30] Ref. [41] African 0.126 0.161 0.145 0.173 0.114 beach 0.217 0.203 0.138 0.221 0.116 building 0.244 0.191 0.132 0.197 0.860 bus 0.189 0.126 0.194 0.132 0.186 dinosaur 0.172 0.109 0.200 0.113 0.196 elephant 0.186 0.163 0.161 0.159 0.116 flower 0.212 0.129 0.189 0.134 0.166 horse 0.207 0.144 0.181 0.146 0.136 mountain 0.227 0.236 0.152 0.229 0.920 food 0.163 0.148 0.172 0.135 0.106 average 0.194 0.161 0.166 0.164 0.131 From Table 2, it can be observed that the proposed strategy outperforms other methods for overall average precision. The proposed strategy achieved superior results for most of the categories from the Corel-1k dataset. The obtained average accuracy is 85% that is sufficiently superior than the art methods [30, 39-41] as they have 76%, 83%, 76%, and 65% reported accuracies, respectively. For some categories such as 'African', the proposed method achieved a lower rate but better than that in [41]. The 'African' category images have multiple objects with several colours that are really challenging to distinguish and segment. The proposed strategy faces deficiency for such images that have too much complexity. From Table 3, the proposed strategy outperforms art methods for overall average recall. The method achieved more superior results and better accuracy of 19% than art methods [30, 39-41]. Same as the precision rate, the recall rate for the 'African' category is lower than other methods due to the high complexity of images. The average precision and recall performance of the proposed strategy for each category are shown in Fig. 8. Fig. 8Open in figure viewerPowerPoint Comparative analysis of average precision and recall for each category of Corel dataset (a) Precision, (b) Recall 3.3.2 Analysis of evaluation for the Oxford flowers dataset In the second experiment, we evaluated the proposed strategy on the Oxford flowers dataset. For this purpose, 170 images are randomly selected from each category and used as a query image. In this experiment, we compared the obtained results with state-of-the-art methods [30, 39, 42, 43]. The obtained result is compared and reported in Table 4 for average precision and in Table 5 for average recall for each group of images, respectively. Table 4. Average precision comparison of different classes in the Oxford flowers dataset #Images/method Our method Ref. [39] Ref. [42] Ref. [30] Ref. [43] 20 0.842 0.821 0.603 0.830 0.793 30 0.811 0.804 0.491 0.812 0.765 40 0.798 0.767 0.484 0.797 0.742 50 0.737 0.706 0.479 0.732 0.687 60 0.724 0.687 0.458 0.703 0.656 70 0.693 0.665 0.436 0.683 0.632 80 0.672 0.642 0.427 0.659 0.605 90 0.682 0.614 0.391 0.634 0.583 100 0.593 0.589 0.372 0.596 0.556 Table 5. Average recall comparison of different classes in the Oxford flowers dataset #Images/method Our method Ref. [39] Ref. [42] Ref. [30] Ref. [43] 20 0.191 0.173 0.124 0.173 0.161 30 0.257 0.261 0.159 0.221 0.241 40 0.314 0.324 0.193 0.197 0.304 50 0.359 0.413 0.235 0.132 0.386 60 0.436 0.452 0.246 0.135 0.418 70 0.529 0.489 0.263 0.113 0.435 80 0.568 0.542 0.303 0.159 0.476 90 0.618 0.621 0.332 0.134 0.527 100 0.691 0.645 0.387 0.146 0.549 From Table 4, it can be observed that our proposed strategy outperforms state-of-the-art methods for average precision. The proposed strategy achieved superior results for the average of most query images than art methods. From Table 5, our proposed strategy also outperforms state-of-the-art methods in terms of recall. The comparative analysis of average precision and recall rates for both the datasets verifies that the proposed strategy is more robust and accurate than most of the art methods. One of the factors affecting the retrieval performance is the larger size of detected keypoints vocabulary. In order to optimise the retrieval performance, our method sustained a good stability between the detected features and size of the vocabulary. 4 Conclusion This work investigates an effective image retrieval strategy based on low-level visual salient features and colour segmentation. The key idea is to extract low-level visual keypoints and quantise into a feature vector in the first step, and extract colour information and quantise into a feature vector in the second step. The extraction of low-level salient keypoints is achieved using the FAST feature descriptor that is well known for its speed and accuracy. The colour information and segmentation are achieved using the L*a*b* colour space that is extensively used for several real-time applications. We compute and combine the similarity of both the feature vectors. The top-rank images are retrieved on the basis of the similarity metric. The experiments are conducted on two commonly used datasets. The experimental results demonstrate that the proposed strategy is robust and accurate than state-of-the-art methods. 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