This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and a bitmap image, which are further processed using vector quantization (VQ) to generate the image feature descriptor. Herein two features are introduced, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), to measure the similarity between a query image and the target image in database. The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF can be utilized to measure the similarity between two images. As documented in the experimental result, the proposed indexing method outperforms the former block truncation coding based image indexing and the other existing image retrieval schemes with natural and textural data sets. Thus, the proposed EDBTC is not only examined with good capability for image compression but also offers an effective way to index images for the content-based image retrieval system.

Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

With the development of information technology, image information has become the main content of network information transmission. With the development of image encryption technology, it is also about the development of image information theft technology. In order to cope with the evolving information theft technology, we must seek a better image encryption algorithm. Among many algorithms, due to the superiority of chaos technology, when the image is encrypted with chaos technology, the ciphertext presents a randomness, which makes the possibility of deciphering greatly reduced. Therefore, the research of digital image encryption algorithm based on chaos technology has become an important means of modern digital image encryption. In this paper, the digital image encryption technology is studied with the dual logistic chaotic map as a tool. The simulation experiments are carried out by using the classical Lena image and the life picture, and the results are analyzed from the histogram, pixel correlation, information entropy, key space size, key sensitivity, and so on. The results show that the method used in this paper has a better security effect.

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Research on digital image encryption algorithm based on double logistic chaotic map

Global Correlation Descriptor

Image content analysis plays a dynamic role in various computer vision applications. These contents can be either visual (i.e. color, shape, texture) or the textual (i.e. text appearing within images). Both the contents involve fundamental characteristics of an image and thus can be an enormous asset for any intelligent application. For content based image retrieval (CBIR) systems, most of the art methods are either annotated text based or the visual search based. Due to high demand of multitasking, there is a great need of a system that can combine visual as well as textual features. Consequently, this work proposes a decisive CBIR approach that combines visual and textual features to retrieve similar images. Firstly, the method classifies the query image as textual and non-textual. If any text appears within the image then the query image is classified as textual, and the text is detected and formed as Bag of Textual words. If the query image is classified as non-textual, the visual salient features are extracted and formed as Bag of Visual words. Next, the method fuses the visual and textual features, and top similar images are retrieved based on the fused feature vector. It supports three modes of retrieval: Image query, Keywords, and a combination of both. The experimental results on four datasets show the efficiency and accuracy of the proposed approach for visual and textual images.

A decisive content based image retrieval approach for feature fusion in visual and textual images

This work addresses the problem of searching and retrieving similar textual images based on the detected text and opens the new directions for textual image retrieval. For image retrieval, several methods have been proposed to extract visual features and social tags; however, to extract embedded and scene text within images and use that text as automatic keywords/tags is still a young research field for text-based and content-based image retrieval applications. The automatic text detection retrieval is an emerging technology for robotics and artificial intelligence. In this study, the authors have proposed a novel approach to detect the text in an image and exploit it as keywords and tags for automatic text-based image retrieval. First, text regions are detected using maximally stable extremal region algorithm. Second, unwanted false positive text regions are eliminated based on geometric properties and stroke width transform. Next, the true text regions are proceeded into optical character recognition for recognition. Third, keywords are formed using a neural probabilistic language model. Finally, the textual images are indexed and retrieved based on the detected keywords. The experimental results on two benchmark datasets show the dominancy of text is efficient and valuable for image retrieval specifically for textual images.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5277

Detected text-based image retrieval approach for textual images

The method for image retrieval based on multi-factors correlation utilizing block truncation coding

Visual and textual information fusion using Kernel method for content based image retrieval

A novel binary image digital watermarking algorithm based on DWT and chaotic encryption is proposed. The proposed scheme utilized the Logistic chaotic sequences and generalized cat mapping to scramble the watermarks, so that the security of embedding watermarks was enhanced. The improved binary method was utilized and watermarks are embedded to the low frequency coefficients of deep wavelet domain. The original image is not required for detecting the watermarks .Using the statistical method the veracity is improved. The experimental results show that the watermark is robust against the cut attack, salt-pepper noise attack, JPEG and et al.

Xingyuan Wang

Papers

A decisive content based image retrieval approach for feature fusion in visual and textual images

The method for image retrieval based on multi-factors correlation utilizing block truncation coding

Visual and textual information fusion using Kernel method for content based image retrieval

Detected text-based image retrieval approach for textual images

A Novel Binary Image Digital Watermarking Algorithm Based on DWT and Chaotic Encryption