Abstract
Content-based image retrieval (CBIR) extracts visual content features (such as color, texture, and shape) of a sample image to retrieve another similar image. Due to the existence of the semantic gap, retrieval results are often unsatisfactory. A CBIR method based on relevance feedback (RF) can reduce the semantic gap and achieve a high-retrieval accuracy by establishing a correlation between low-level image features and high-level semantics via human-computer interaction. However, the complicated human-computer interface of RF increases the burden on users; hence, some scholars have proposed the pseudo-relevance feedback (PRF) technology. To further contribute to the research, this paper proposes a self-feedback image retrieval algorithm based on annular color moments. In this approach, hashing sequences of color moments based on annular segmentation are extracted to be used as feature vectors for initial retrieval. Based on this result, improved subtractive clustering and correlation feedback techniques are used for extended queries. Thus, a self-feedback method without user participation is realized. The experimental results show that the accuracy of image retrieval can be improved, and the proposed algorithm is robust to image rotation, scaling, and translation.
Highlights
In the era of Web 2.0, especially with the popularity of social networking sites such as Flickr and Facebook, unstructured data such as images, videos, and audios are growing at an alarming rate every day
Based on the above research, this paper proposes a method for image retrieval self-feedback based on fuzzy clustering and the virtual relevance feedback technology
The color moments based on annular segmentation were used to describe the color images, and the red, green, and blue components of the pixels were fully utilized to improve the performance of the method
Summary
In the era of Web 2.0, especially with the popularity of social networking sites such as Flickr and Facebook, unstructured data such as images, videos, and audios are growing at an alarming rate every day. Techniques for content-based image retrieval (CBIR) use only visual features of an image as a query, storing them in an image feature library [1]. Since these features are usually high-dimensional, storing and retrieving massive visual features are the main challenges for developing the CBIR technology. Hashing is one of the emerging technologies for supporting fast and accurate image retrieval that can be applied as an effective technique for CBIR [2]. The core idea is to map high-dimensional visual features to compact binary codes in the low-dimensional Hamming space, so that visual similarities of images can be efficiently measured using simple yet efficient bit operations.
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