With the great demand for storing and transmitting images as well as their managing, the retrieval of compressed images is a field of intensive research. While most of the works have been devoted to the case of losslessly encoded images (by extracting features from the unquantized transform coefficients), new studies have shown that lossy compression has a negative impact on the performance of conventional retrieval systems. In this work, we investigate three different quantization schemes and propose for each one an efficient retrieval approach. More precisely, the uniform quantizer, the moment preserving quantizer and the distribution preserving quantizer are considered. The inherent properties of each quantizer are then exploited to design an efficient retrieval strategy, and hence, to reduce the drop of retrieval performances resulting from the quantization effect. Experimental results, carried out on three standard texture databases and a color dataset, show the benefits which can be drawn from the proposed retrieval approaches.

Efficient transform-based texture image retrieval techniques under quantization effects

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.

Multi-objective whale optimization algorithm for content-based image retrieval

In last few decades. Content Based Image Retrieval System (CBIR) is an emerging field to retrieve relevant images from a database. It utilizes the visual contents of an image for the local and global features. Local feature includes spatial domain which presents the significance of the image as well as the index of an image. Global feature includes shape descriptors, contour representations and texture features. Segmentation process is required in global feature extraction technique. It is a challenging task to simulate visual information in CBIR system. CBIR strategy combines the local and global features to deal with the low level information. In this paper, we proposed new CBIR technique to fuse color and texture features. Color Histogram (CH) is used to extract a color information. Texture features are extracted by Discrete Wavelet Transform (DWT) and Edge Histogram Descriptor (EDH). The features are created for each image and stored as a feature vector in the database. We evaluated our work using Corel 1-k dataset. To examine the accuracy with the other proposed systems, precision and recall methods are used that provides competitive and efficient result. The experimental results show that our proposed method outperforms with existing CBIR systems.

Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor

An improved radial basis function neural network for object image retrieval

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature "Color Coherence Vectors," the texture feature "Gray Level Co-Occurrence Matrix," and a special image feature "Edge Histogram Descriptor" are then exacted from these patches to generate a high-dimensional matrix called the "feature matrix." Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79-0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

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Wheat ears counting in field conditions based on multi-feature optimization and TWSVM.

This paper describes an efficient algorithm for Content Based Image Retrieval (CBIR) based on Discrete Wavelet Transform (DWT) and Edge Histogram Descriptor (EHD) feature of MPEG-7. The proposed algorithm is explained for image retrieval based on shape and texture features only not on the basis of color information. Here input image is first decomposed into wavelet coefficients. These wavelet coefficients give mainly horizontal, vertical and diagonal features in the image. After wavelet transform, Edge Histogram Descriptor is then used on selected wavelet coefficients to gather the information of dominant edge orientations. The combination of DWT and EHD techniques increases the performance of image retrieval system for shape and texture based search. The performance of various wavelets is also compared to find the suitability of particular wavelet function for image retrieval. The proposed algorithm is trained and tested for Wang image database. The results of retrieval are expressed in terms of Precision and Recall and compared with various other proposed schemes to show the superiority of our scheme.

S. Agarwal

Papers

Content Based Image Retrieval using Discrete Wavelet Transform and Edge Histogram Descriptor