Abstract
Tourist image retrieval has attracted increasing attention from researchers. Mainly, supervised deep hash methods have significantly boosted the retrieval performance, which takes hand-crafted features as inputs and maps the high-dimensional binary feature vector to reduce feature-searching complexity. However, their performance depends on the supervised labels, but few labeled temporal and discriminative information is available in tourist images. This paper proposes an improved deep hash to learn enhanced hash codes for tourist image retrieval. It jointly determines image representations and hash functions with deep neural networks and simultaneously enhances the discriminative capability of tourist image hash codes with refined semantics of the accompanying relationship. Furthermore, we have tuned the CNN to implement end-to-end training hash mapping, calculating the semantic distance between two samples of the obtained binary codes. Experiments on various datasets demonstrate the superiority of the proposed approach compared to state-of-the-art shallow and deep hashing techniques.
Highlights
With the rise of cheap sensors, mobile terminals, and social networks, research on tourist images is making good progress, which results in an explosive growth of image retrieval in social networks. is trend imposes great challenges on developing scalable indexing approaches, supporting retrieving relevant images of such massive tourist images
We propose an architecture of deep convolution networks designed for hash learning, which has substantially superior performance on large-scale tourist images by end-to-end learning discriminative short binary code
We assess the performance in terms of mean average precision (MAP), calculated for all returned samples by sorting with the Hamming distance. e MAP value is shown in Table 2, where DNNH, DLBHC, and the proposed method are deep hashing methods, while the other ways are traditional hashing methods
Summary
With the rise of cheap sensors, mobile terminals, and social networks, research on tourist images is making good progress, which results in an explosive growth of image retrieval in social networks. is trend imposes great challenges on developing scalable indexing approaches, supporting retrieving relevant images of such massive tourist images. SIFT [1] uses local descriptors to encode image regions of interest, for example, HOG [2] and BOW [3]. It is highly dependent on the availability and quality of tags. Due to the fast query speed and low storage cost, learning-based hash has been attracting research interests and was applied to applications such as large-scale object retrieval [4], image classification [5], and detection [3]. Convolutional neural network hashing (CNNH) [8] incorporates deep neural networks into hash coding to learn the image representations and hash codes. Other hashing methods have been proposed [11,12,13]
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