Mobile multi-view object image search

Calisir, Fatih; Baştan, Muhammet; Ulusoy, Özgür; Güdükbay, Uğur

doi:10.1007/s11042-016-3659-9

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Convolutional Network Research With the development of social networks in recent years, it is easier to obtain text information related to the image from the user, such as comments and tags, from users during the process of sharing pictures on the Internet [12]. Artificial annotation semantics technology allows the extraction of semantic concepts related to images from textual information.…”

Section: Cross-modal Information Retrieval Andmentioning

confidence: 99%

Graph Convolutional Networks for Cross-Modal Information Retrieval

Yang

Zhang

2022

Wireless Communications and Mobile Computing

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In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.

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Section: Cross-modal Information Retrieval Andmentioning

confidence: 99%

Graph Convolutional Networks for Cross-Modal Information Retrieval

Yang

Zhang

2022

Wireless Communications and Mobile Computing

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“…To assess the capability of deep CNN features in retrieving color images in response to partially colored sketches, we collected more than 35,000 color images from various datasets, corresponding to the 250 categories of TU Berlin sketches dataset. These images were gathered from Corel-10k dataset [ 31 ], Multi-view objects dataset [ 32 ], and Caltech256 [ 33 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

2017

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In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods.

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