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was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content-based searching. In this paper we survey some technical aspects of current content-based image retrieval systems based on several neural network architectures. Firstly we discuss the image retrieval system based on
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the
In this paper, a novel method is proposed using color and pattern information for recognizing some emotions included in a textile. Here we use 10 Kobayashi emotion keywords. Our method is composed of feature extraction and classification. For accurate emotion recognition, both color and pattern are extracted from a
use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only color information but also spectrum and keywords are employed. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed system. Moreover, in the
A neural network model with adaptive structure for image annotation is proposed in this paper. The adaptive structure enables the proposed model to utilize both global and regional visual features, as well as correlative information of annotated keywords for annotation. In order to achieve an approximate global
network for labeling images with emotional keywords based on visual features only and examine an influence of used emotion filter on process of similar images retrieval. The performed experiments have shown that use of the emotion filter increases performance of the system for around 10 percent. points.
knowledge are from visual images. Users simply submit the desired image keywords, such as elegant, sporty, casual, and occasion type, such as formal meeting, outdoor dating, to the system. Then sensational cognition model is activated to search the desired clothes within the personal garment database. Category learning with
simply submit the desired image keywords, such as elegant, sporty, casual, and so on, and occasion type, such as formal meeting, outdoor dating, and so on, to the system. And then the fashion style recognition module is activated to search the desired clothes within the personal garment database. Category learning with
retrieval system from plural key images using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only color information but also spectrum, impression words and keywords are employed. In the proposed system
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