Abstract
A web-based search system recommends and gives results such as customized image or video contents using information such as user interests, search time, and place. Time information extracted from images can be used as a important metadata in the web search system. We present an efficient algorithm to classify time period into day, dawn, and night when the input is a single image with a sky region. We employ the Mask R-CNN to extract a sky region. Based on the extracted sky region, reference color histograms are generated, which can be considered as the ground-truth. To compare the histograms effectively, we design the windowed-color histograms (for RGB bands) to compare each time period from the sky region of the reference data with one of the input images. Also, we use a weighting approach to reflect a more separable feature on the windowed-color histogram. With the proposed windowed-color histogram, we verify about 91% of the recognition accuracy in the test data. Compared with the existing deep neural network models, we verify that the proposed algorithm achieves better performance in the test dataset.
Highlights
We design a web-based search system to get accurate answers for the given queries
We propose an efficient algorithm to classify time by analyzing the sky region with deep learning and color histogram
We propose an efficient algorithm to classify time by analyzing the color histogram of the sky region
Summary
We design a web-based search system to get accurate answers for the given queries. By expanding the definition of semantic search, the searching service is developing into a customized service that shows objective results and recommends information that users may like [5] They recommend and show customized image or video content using information such as user interests, search time, climate, and the place where the user resides. By using contextual information based on image or video data, it has so far been successfully applied to computer vision tasks such as object detection, semantic segmentation, and image classification [6,7,8,9,10,11,12]. This information can describe objects’ class, background, as well as some situations or relationship between them [13,14,15]
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