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This book summarizes The primary concern of supervised hashing is to convert the original features into short binary codes that can maintain label similarity in the Hamming space. Due to their strong generalization capabilities, non-linear hash functions have shown to be superior than linear ones. Kernel functions are frequently utilized in the literature to create non-linear hashing, which results in encouraging retrieval performance but long evaluation and training times. Here, we suggest using boosted decision trees, which are quick to train and assess and are hence more suited for hashing…mehr

Produktbeschreibung
This book summarizes The primary concern of supervised hashing is to convert the original features into short binary codes that can maintain label similarity in the Hamming space. Due to their strong generalization capabilities, non-linear hash functions have shown to be superior than linear ones. Kernel functions are frequently utilized in the literature to create non-linear hashing, which results in encouraging retrieval performance but long evaluation and training times. Here, we suggest using boosted decision trees, which are quick to train and assess and are hence more suited for hashing with high dimensional data. As part of continuous improvement, we first suggest sub-modular formulations for the hashing binary code inference issue as well as an effective block search technique based on Graph Cut for large-scale inference. Then, we train boosted decision trees to suit the binary codes in order to learn hash functions. Experiments show that in terms of retrieval precision and training duration, our suggested strategy greatly surpasses the majority of state-of-the-art methods.
Autorenporträt
El Dr. M. Aravind Kumar obtuvo su licenciatura en ECE, su máster en diseño de sistemas VLSI en JNTUH y su doctorado en la Universidad GITAM de Visakhapatnam. Trabaja como director en el INSTITUTO DE CIENCIA E INGENIERÍA DE GODAVARI OCCIDENTAL. Tiene 15 años de experiencia docente. Es miembro vitalicio de FIE, ISTE, TETE, SCIEI, UACEE e IAENG.