The local descriptors have been the backbone of most of the computer vision problems. Most of the existing local descriptors are generated over the raw input images. In order to increase the discriminative power of the local descriptors, some researchers converted the raw image into multiple images with the help of some high and low pass frequency filters, then the local descriptors are computed over each filtered image and finally concatenated into a single descriptor. By doing so, these approaches do not utilize the inter frequency relationship which causes the less improvement in the discriminative power of the descriptor that could be achieved. In this paper, this problem is solved by utilizing the decoder concept of multi-channel decoded local binary pattern over the multi-frequency patterns. A frequency decoded local binary pattern (FDLBP) is proposed with two decoders. Each decoder works with one low frequency pattern and two high frequency patterns. Finally, the descriptors from both decoders are concatenated to form the single descriptor. The face retrieval experiments are conducted over four benchmarks and challenging databases such as PaSC, LFW, PubFig, and ESSEX. The experimental results confirm the superiority of the FDLBP descriptor as compared to the state-of-the-art descriptors such as LBP, SOBEL_LBP, BoF_LBP, SVD_S_LBP, mdLBP, etc.
The local binary pattern (LBP) is a widely adapted local descriptor @cite_22 . It encodes the local relationship of the pixels. Basically, it finds a binary pattern for each pixel and represent it into the histogram form. A binary pattern for a pixel consists of the 8 binary values corresponding to 8 local neighbors of the concerned pixel distributed in a circular fashion. For a neighbor, the binary value is 1 if the intensity value of neighboring pixel is greater than or equal to the intensity value of the center pixel, otherwise the binary value is 0. Originally, LBP was proposed for the texture classification @cite_22 . However, later on, it is proved as the foundation to solve many computer vision problems @cite_30 . Several variants of LBP have been investigated by several researchers for various problems such as local image matching @cite_33 , content based image retrieval @cite_5 , @cite_17 , @cite_46 , texture classification @cite_7 , @cite_3 , @cite_39 , biomedical image analysis @cite_4 , @cite_47 , @cite_51 , @cite_14 , etc.
@cite_30 @cite_47 @cite_14 @cite_4 @cite_22 @cite_33 @cite_7 @cite_3 @cite_39 @cite_5 @cite_46 @cite_51 @cite_17
33189562 2473341269 2411206587 2023234912 2163352848 1979075587 2103496373 2028735463 2053544201 2145947562 2409822643 1961043134 2342423902
Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. A novel image feature descriptor based on the local bit-plane decoded pattern (LBDP) is introduced for indexing and retrieval of biomedical images in this paper. A local bit-plane transformation scheme is proposed to compute the local bit-plane transformed values for each image pixel from the bit-plane binary contents of its each neighboring pixels. The introduced LBDP is generated by finding a binary pattern using the difference of center pixel's intensity value with the local bit-plane transformed values. The efficacy of the LBDP is tested under biomedical image retrieval using average retrieval precision and average retrieval rate. Three benchmark databases Emphysema-CT, NEMA-CT, and Open Access Series of Imaging Studies magnetic resonance imaging are used for the evaluation and comparison of the proposed approach with recent state-of-art methods. The experimental results confirm the discriminative ability and the efficiency of the proposed LBDP for biomedical image indexing and retrieval and prove the outperformance of existing biomedical image retrieval approaches.