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Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition

The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained image recognition. Unfortunately, user click frequency data are usually absent in practice. It remains challenging to predict the click feature from the visual feature, because the user click frequency vector of an image is always noisy and sparse. In this paper, we devise a H ierarchical D eep W ord E mbedding (HDWE) model by integrating sparse constraints and an improved RELU operator to address click feature prediction from visual features. HDWE is a coarse-to-fine click feature predictor that is learned with the help of an auxiliary image dataset containing click information. It can therefore discover the hierarchy of word semantics. We evaluate HDWE on three dog and one bird image datasets, in which Clickture-Dog and Clickture-Bird are utilized as auxiliary datasets to provide click data, respectively. Our empirical studies show that HDWE has 1) higher recognition accuracy, 2) a larger compression ratio, and 3) good one-shot learning ability and scalability to unseen categories.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.2 February 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 563-578

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TRANSFER LEARNING
HIERARCHICAL MODEL
DEEP NEURAL NETWORK
WORD EMBEDDING
CLICK PREDICTION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2019.2932058

Pernyataan Tanggungjawab

Jun Yu, Min Tan, Hongyuan Zhang, Yong Rui, Dacheng Tao

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