Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, i.e., keyword-based search by title or author. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a manga-specific image retrieval system. The proposed system consists of efficient margin labeling, edge orientation histogram feature description with screen tone removal, and approximate nearest-neighbor search using product quantization. For querying, the system provides a sketch-based interface. Based on the interface, two interactive reranking schemes are presented: relevance feedback and query retouch. For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research. Experimental results showed that the proposed framework is efficient and scalable (70 ms from 21,142 pages using a single computer with 204 MB RAM).
In the literature of content-based image retrieval, many methods have been proposed to describe images. A query and dataset images are converted to some form of vector representation to evaluate their similarity. A typical form is the bag-of-features (BoF) representation @cite_1 , in which local features such as SIFT @cite_41 are extracted from an image and quantized into a sparse histogram. The distance between a query histogram and a dataset histogram can be computed efficiently using an inverted index structure. The BoF form has been studied widely because it achieved successful image description with fast computation.
@cite_41 @cite_1
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The past decade has seen the growing popularity of Bag of Features (BoF) approaches to many computer vision tasks, including image classification, video search, robot localization, and texture recognition. Part of the appeal is simplicity. BoF meth- ods are based on orderless collections of quantized local image descriptors; they discard spatial information and are therefore conceptually and computationally simpler than many alternative methods. Despite this, or perhaps because of this, BoF-based systems have set new performance standards on popular image classification benchmarks and have achieved scalability breakthroughs in image retrieval. This paper presents an introduction to BoF image representations, describes critical design choices, and surveys the BoF literature. Emphasis is placed on recent techniques that mitigate quantization errors, improve fea- ture detection, and speed up image retrieval. At the same time, unresolved issues and fundamental challenges are raised. Among the unresolved issues are determining the best techniques for sampling images, describing local image features, and evaluating system performance. Among the more fundamental challenges are how and whether BoF meth- ods can contribute to localizing objects in complex images, or to associating high-level semantics with natural images. This survey should be useful both for introducing new in- vestigators to the field and for providing existing researchers with a consolidated reference to related work. This paper presents a Bag of Visual Words (BoVW) based approach to retrieve similar word images from a large database, efficiently and accurately. We show that a text retrieval system can be adapted to build a word image retrieval solution. This helps in achieving scalability. We demonstrate the method on more than 1 Million word images with a sub-second retrieval time. We validate the method on four Indian languages, and report a mean average precision of more than 0.75. We represent the word images as histogram of visual words present in the image. Visual words are quantized representation of local regions, and for this work, SIFT descriptors at interest points are used as feature vectors. To address the lack of spatial structure in the BoVW representation, we re-rank the retrieved list. This significantly improves the performance. Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extracted from them. The visual codebook is generally constructed by using an unsupervised algorithm such as K-means to quantize the local descriptors into clusters. Images are then represented by the frequency histograms of the codewords contained in them. To build a compact and discriminative codebook, codeword selection has become an indispensable tool. However, most of the existing codeword selection algorithms are supervised and the human labeling may be very expensive. In this paper, we consider the problem of unsupervised codeword selection, and propose a novel algorithm called Discriminative Codeword Selection (DCS). Motivated from recent studies on discriminative clustering, the central idea of our proposed algorithm is to select those codewords so that the cluster structure of the image database can be best respected. Specifically, a multi-output linear function is fitted to model the relationship between the data matrix after codeword selection and the indicator matrix. The most discriminative codewords are thus defined as those leading to minimal fitting error. Experiments on image retrieval and clustering have demonstrated the effectiveness of the proposed method. This paper proposes a new template matching method that is robust to outliers and fast enough for real-time operation. The template and image are densely transformed in binary code form by projecting and quantizing histograms of oriented gradients. The binary codes are matched by a generic method of robust similarity applicable to additive match measures, such as L p - and Hamming distances. The robust similarity map is computed efficiently via a proposed Inverted Location Index structure that stores pixel locations indexed by their values. The method is experimentally justified in large image patch datasets. Challenging applications, such as intra-category object detection, object tracking, and multimodal image matching are demonstrated.