Techniques to learn hash codes which can store and retrieve large dimensional multimedia data efficiently have attracted broad research interests in the recent years. With rapid explosion of newly emerged concepts and online data, existing supervised hashing algorithms suffer from the problem of scarcity of ground truth annotations due to the high cost of obtaining manual annotations. Therefore, we propose an algorithm to learn a hash function from training images belonging to 'seen' classes which can efficiently encode images of 'unseen' classes to binary codes. Specifically, we project the image features from visual space and semantic features from semantic space into a common Hamming subspace. Earlier works to generate hash codes have tried to relax the discrete constraints on hash codes and solve the continuous optimization problem. However, it often leads to quantization errors. In this work, we use the max-margin classifier to learn an efficient hash function. To address the concern of domain-shift which may arise due to the introduction of new classes, we also introduce an unsupervised domain adaptation model in the proposed hashing framework. Results on the three datasets show the advantage of using domain adaptation in learning a high-quality hash function and superiority of our method for the task of image retrieval performance as compared to several state-of-the-art hashing methods.

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.

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Advancement in electronics industry and revolution in information technology sector has lead authentication and recognition to be the important aspect in today's life. Authentication and recognition can be achieved using biometric cues which provide secure and reliable alternative to traditional methods of human identification. Human iris is most reliable biometric because of its uniqueness, stability and non-invasive nature. The paper presents an efficient iris recognition system for authentication of a person using block based approach with Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction and standard deviation method for feature comparison. The proposed system is evaluated for its performance i.e. identification of person on 500 images of iris of 100 persons (5 images of each person) of UBIRIS v2 database. The experimental results prove the efficiency of block based approach with DWT is superior to traditional approach of DWT and DCT. The false acceptance rate (FAR) and false rejection rate (FRR) is minimum for block based DWT.

A new species of Protosticta Selys, 1885 (Odonata: Zygoptera: Platystictidae) is described based on two male specimens collected from Kerala, at the southern end of the Western Ghats in India. We compared P. armageddonensis sp. nov. with the three closely similar Protosticta species recently described from the Western Ghats, namely P.anamalaica Sadasivan, Nair & Samuel, 2022, P. cyanofemora Joshi, Subramanian, Babu & Kunte, 2020, and P. monticola Emiliyamma & Palot, 2016, to provide comprehensive differential diagnosis. The new species is distinguished from its congeners by a combination of characters, including the structure of prothorax, caudal appendages, genital ligula, and markings on the 8th abdominal segment. A revised key of Protosticta spp. of the Western Ghats, based on mature male specimens is provided.