Feature Descriptor For Image Retrieval Research Articles

A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern

In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of bins and performed the voting with saturation values and vice versa by following a principle similar to that of the HOG descriptor, where orientation of the gradient is quantized into a certain number of bins and voting is done with gradient magnitude. This helps us to study the nature of variation of saturation with variation in Hue and nature of variation of Hue with the variation in saturation. The texture component of our descriptor considers the co-occurrence relationship between the pixels symmetric about both the diagonals of a 3 × 3 window. Our work is inspired from the work done by Dubey et al. (IEEE Signal Process Lett 22(9):1215–1219, [2015]). These two components, viz. color and texture information individually perform better than existing texture and color descriptors. Moreover, when concatenated the proposed descriptors provide a significant improvement over existing descriptors for content base color image retrieval. The proposed descriptor has been tested for image retrieval on five databases, including texture image databases—MIT-VisTex database and Salzburg texture database and natural scene databases Corel 1K, Corel 5K and Corel 10K. The precision and recall values experimented on these databases are compared with some state-of-art local patterns. The proposed method provided satisfactory results from the experiments.

Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval

In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3 × 3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bit pattern. It ignores the effect of the adjacent neighbors of a particular pixel for its binary encoding and also for texture description. The proposed method is based on the concept that neighbors of a particular pixel hold significant amount of texture information that can be considered for efficient texture representation for CBIR. The main impact of utilizing the mutual relationship among adjacent neighbors is that we do not rely on the sign of the intensity difference between central pixel and one of its neighbors (Ii) only, rather we take into account the sign of difference values between Ii and its adjacent neighbors along with the central pixels and same set of neighbors of Ii. This makes our pattern more resistant to illumination changes. Moreover, most of the local patterns including LBP concentrates mainly on the sign information and thus ignores the magnitude. The magnitude information which plays an auxiliary role to supply complementary information of texture descriptor, is integrated in our approach by considering the mean of absolute deviation about each pixel Ii from its adjacent neighbors. Taking this into account, we develop a new texture descriptor, named as Local Neighborhood Intensity Pattern (LNIP) which considers the relative intensity difference between a particular pixel and the center pixel by considering its adjacent neighbors and generate a sign and a magnitude pattern. Finally, the sign pattern (LNIPS) and the magnitude pattern (LNIPM) are concatenated into a single feature descriptor to generate a more effective feature descriptor. The proposed descriptor has been tested for image retrieval on four databases, including three texture image databases - Brodatz texture image database, MIT VisTex database and Salzburg texture database and one face database - AT&T face database. The precision and recall values observed on these databases are compared with some state-of-art local patterns. The proposed method showed a significant improvement over many other existing methods.

An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP.

Multi-trend binary code descriptor: a novel local texture feature descriptor for image retrieval

With the development of image vision technology, local descriptors have attracted wide attention in the fields of image retrieval and classification. Even though varieties of methods based on local descriptor have achieved excellent performance, most of them cannot effectively represent the trend of pixels change, and they neglect the mutual occurrence of patterns. Therefore, how to construct local descriptors is of vital importance but challenging. In order to solve this problem, this paper proposes a multi-trend binary code descriptor (MTBCD). MTBCD mimics the visual perception of human to describe images by constructing a set of multi-trend descriptors which are encoded with binary codes. The method exploits the trend of pixels change in four symmetric directions to obtain the texture feature, and extracts the spatial correlation information using co-occurrence matrix. These intermediate features are integrated into one histogram using a new fusion strategy. The proposed method not only captures the global color features, but also reflects the local texture information. Extensive experiments have demonstrated the excellent performance of the proposed method.

Fuzzy-linked phase congruency-based feature descriptors for image retrieval

ABSTRACTThe demand for acute and compact feature descriptors remains a key issue of concern for prevailing image retrieval schemes. This paper offers dual feature descriptors characterised by phase congruency (PC) and fuzzy logic for image indexing and retrieval. The proposed mechanism commences with colour space conversion of RGB query images to L*a*b* triplets and further application of PC generates relevant feature information. The ensuing visual features are blended by fuzzy rules to formulate the unified feature histograms and later fuzzy quantised to produce two feature descriptors termed as PC-based colour edge directivity descriptor (PC-CEDD), PC-based fuzzy colour texture histogram (PC-FCTH). The resulting descriptors occupy minimal storage space of 23–74 bytes per image, with 60% reduction in feature extraction time in comparison with CEDD, FCTH. Relative precision–recall and mean average precision (MAP) analysis of the intended feature histograms on medical, texture, and object picture dataset signify the improvement in retrieval performance. Furthermore, average normalised modified retrieval rank analysis of the intended descriptors reveals the better matching quality of the given query image.

Local tri-directional patterns: A new texture feature descriptor for image retrieval

Texture is a prominent feature of image and very useful in feature extraction for image retrieval application. Statistical and structural patterns have been proposed for image retrieval and browsing. In the proposed work, a new texture feature descriptor is developed. The proposed method uses local intensity of pixels based on three directions in the neighborhood and named as the local tri-directional pattern (LTriDP). Also, one magnitude pattern is merged for better feature extraction. The proposed method is tested on three databases, in which first two, Brodatz texture image database and MIT VisTex database are texture image databases and third one is the AT&T face database. Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for image retrieval application.