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A major component of a generic image retrieval pipeline is producing concise and effective descriptors for each image. Previous works have shown impressive results in image retrieval when using descriptors from the black-box output of the fully-connected stage of pretrained Convolutional Neural Networks (ConvNets). However, previous work on descriptors pooled from the deep feature maps from late convolutional...
The retrieval of visual cultural symbols is an important research field of inheriting and carrying forward Chinese traditional culture in digital way. Generally visual cultural symbols are foregrounds of natural images, so using shape features in image retrieval that needs image segmentation in advance has great advantages. At present, image segmentation is mostly interactive, which is quite subjective,...
This paper presents a retrieval method for the image of Chinese characters calligraphy. The precision and recall rate are main indicators to measure the quality of image retrieval algorithm. Starting from the two aspects, there are two parts in the process of image retrieve which is proposed in this paper. In this paper, carry out the retrieve with the Hu invariant moment matching algorithm, which...
Visual text information is a descriptive part of many images that can be used to perform mobile visual search (MVS) with particularly small queries. In this paper, we propose a system that uses word patch descriptors for retrieving images containing visual text. A random sampling method is used to find duplicate word patches in the database and reduce the database size. The system achieves comparable...
The Bag-of-Visual-Words model has become a popular model in image retrieval and computer vision. But when the local features of the Interest Points (IPs) are transformed into visual words in this model, the discriminative power of the local features are reduced or compromised. To address this issue, in this paper, we propose a novel contextual descriptor for local features to improve its discriminative...
In this work, we propose a Latent Semantic Association Retrieval(LSAR) method to break the bottleneck of the low-level feature based medical image retrieval. The method constructs the high-level semantic correlations among patients based on the low-level feature set extracted from the images. Specifically, a Pair-LDA model is firstly designed to refine the topic generation process of traditional Latent...
In this paper, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. The approach consists of three primary steps. First, K-means is...
All existing feature point based partial-duplicate image retrieval systems are confronted with the false feature point matching problem. To resolve this issue, geometric contexts are widely used to verify the geometric consistency in order to remove false matches. However, most of the existing methods focus on local geometric contexts rather than global. Seeking global contexts has attracted a lot...
In this paper, we propose a new method for cartoon image retrieval based on the local invariant shape feature, named Scalable Shape Context. The proposed feature uses the Harris-Lap lace corner to localize the key points and corresponding scale in the cartoon image. Then, we use Shape Context to describe the local shape. The feature point matching is achieved by a weighted bipartite graph matching...
Nowadays, the explosive use of mobile phones leads to generate a large number of personal photos. The requirements of effective image retrieval becomes evident. In this paper, we overview image retrieval techniques with special emphasis on different works that consider automatic image annotation as solution to retrieve images in mobile environment. In most automatic image annotation systems, the high-level...
In this paper, we propose a fast large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbours for query signatures. In the first stage, we use M randomly generated hyper planes to separate shape context feature points into different bins, and...
Content-based image retrieval has been suggested as an aid to medical diagnosis. Techniques based on standard feature descriptors, however, might not represent optimally the pathological characteristics in medical images. In this paper, we propose a new approach for medical image retrieval based on pathology-centric feature extraction and representation; and patch-based local feature extraction and...
Image local invariant features have been used in a wide range of applications, e.g., image retrieval, object categorization and robot localization. The matching of local feature points involves a succinct and discriminative descriptor for each point. However, current local descriptors use only neighborhood information, which typically suffer the lack of global context and fail to resolve ambiguities...
This paper presents an image retrieval algorithm based on SIFT operator and the temporal-spatial context semantic, which makes use of the SIFT operator to present image features, and the temporal-spatial contex semantic to establish the knowledge networks. The experiments show that the algorithm has more accurate retrieval accuracy, and a very tight correlation between the cross-media information.
This paper proposes a new method of handwriting reconstruction using a camera pen. We print random dot patterns on the document background to enable retrieval of both the current document and the pen position on this document. Dot arrangements are stored in a hash table using Locally Likely Arrangement Hashing. For retrieval, they are extracted from the camera image and matched to the corresponding...
Having effective methods to access the desired images is essential nowadays with the availability of huge amount of digital images. The proposed approach is based on an analogy between image retrieval containing desired objects (object-based image retrieval) and text retrieval. We propose a higher-level visual representation, for object-based image retrieval beyond visual appearances. The proposed...
In this paper, we develop a novel image representation method which is based firstly on constructing visual words based on a local patch extraction and a fusion of descriptors. The spatial constitution of an image is represented with a mixture of n Gaussians in the feature space. The new spatial weighting scheme consists in weighting visual words according to the probability of each visual word belongs...
The paper presents a new multiple feature fusion (MFF) based on latent semantic indexing (LSI) method to achieve an improved image retrieval performance. The proposed method extracts different physical features, which come from not the whole image but its main objects, and constructs a multi-modal semantic space, each dimension of which represents a different feature component of the image. Furthermore,...
Context plays a valuable role in any image understanding task confirmed by numerous studies which have shown the importance of contextual information in computer vision tasks, like object detection, scene classification and image retrieval. Studies of human perception on the tasks of scene classification and visual search have shown that human visual system makes extensive use of contextual information...
In this work we employ contextual information to improve the quality of image labellings provided by an existing automatic image annotation algorithm in a weakly supervised setting, where each training image is labelled but it is not known which part of the image its labels are referring to. We recast the problem into that of constructing a graph which encodes pairwise consistency of candidate annotations...
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