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Image search engines commonly employ the Bag Of Features (BOF) method to represent each database image with a feature vector and retrieve the best candidate using a measure of similarity to a query image vector. The BOF vector, which specifies the occurrence frequency of features, is used with Soft Assignment (SA) to find the most similar candidates which are further analyzed using geometric information...
Based on a large scale crime scene investigation (CSI) image database, an effective and efficient CSI image retrieval system has been proposed to empower the investigative work of the police force. The main contribution of this paper includes: (1) a DCT domain texture feature extraction algorithm is proposed for CSI images, which is shown to be simple and effective. (2) the use of GIST descriptor...
The power of modern image matching approaches is still fundamentally limited by the abrupt scale changes in images. In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views. Drawing inspiration from the scale space theory, we start with encoding the image’s scale space into a compact multi-scale representation. Then, rather than trying...
Much work has been done on the assessment of texture descriptors for image retrieval in many domains. In this work, we evaluate the accuracy and performance of three wellknown texture descriptors – Gabor Filters, GLCM, and LBP – for seismic image retrieval. These subsurface images pose challenges yet not thoroughly investigated in previous works, which are addressed and evaluated in our experiments...
Nowadays online image search become more essential. In this paper, we have extended existing system for image re-ranking is explained. The existing system is divided into offline and online parts. In offline part various semantic spaces are automatically learns for different query keywords. Image Semantic content as signatures are generated by mapping the image features i.e. visual features into its...
Image annotation is an integral and important task for image retrieval. Automatic image annotation has been studied for quite some time now, but there is still enough scope for improvement considering the challenges associated with it. Existing systems focus on reducing the semantic gap between image and text using various heuristic, probabilistic or learning based approaches. Often, the automatic...
Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheless, human sketches are often highly abstract and iconic, resulting in severe misalignments...
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELE (DEep Local Feature). The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. To identify semantically useful local features for image retrieval, we also propose an attention mechanism for key point selection,...
Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer the 3D structure that bridges images with users text descriptions. However, direct...
In this paper we propose a unified framework to address multiple realistic image retrieval tasks concerning both category and attributes. Considering the scale of modern datasets, hashing is favorable for its low complexity. However, most existing hashing methods are designed to preserve one single kind of similarity, thus incapable of dealing with the different tasks simultaneously. To overcome this...
Querying with an example image is a simple and intuitive interface to retrieve information from a visual database. Most of the research in image retrieval has focused on the task of instance-level image retrieval, where the goal is to retrieve images that contain the same object instance as the query image. In this work we move beyond instance-level retrieval and consider the task of semantic image...
Digital imaging plays an important role in many human activities, such as agriculture and forest management, earth sciences, urban planning, weather forecasting, medical imaging and so on. Processing, exploring and visualizing the inconceivable volumes of such images has turned out to be progressively troublesome. The Content-Based Image Retrieval (CBIR) remains an important issue that finds potential...
Automatic Image Annotation (AIA) is a challenging problem in the field of image retrieval, and several methods have been proposed. However, visually supporting this important tasks and reducing the semantic gap between low-level image features and high-level semantic concepts still remains a key issue. In this paper, we propose a visually supporting image annotation framework based on visual features...
In this paper, we propose an improved image retrieval method, dedicated to images of buildings/landmarks from urban environments. Locally detected key points are binary labelled as building or no-building using a SVM-based classifier. Thereafter, only key points labelled as building are retained. In this way, the data in the database vocabulary is reduced to only the relevant one and solely the relevant...
Several recent works interpret convolutional features produced by deep convolutional neural networks as local descriptors. Existing high-dimensional aggregation based methods, e.g., Fisher Vector (FV) obtain inferior performance to pooling based methods in most situations, and we observe that it is mainly caused by the ignorance of spatial weights. In this paper, we propose a novel method named spatial...
Hashing has been recognized as one of the most promising ways in indexing and retrieving high-dimensional data due to the excellent merits in efficiency and effectiveness. Nevertheless, most existing approaches inevitably suffer from the problem of “semantic gap”, especially when facing the rapid evolution of newly-emerging “unseen” categories on the Web. In this work, we propose an innovative approach,...
Neural activations produced by deep convolutional networks have recently become state-of-the-art representation for image retrieval. To obtain a global image representation, sum-pooling has been frequently used to aggregate activations of convolutional feature maps. This work first presents an understanding on the effectiveness of sum-pooling via probabilistic interpretation, by proving that sum-pooling...
A novel scheme with deep cross-modal correlation learning is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR) for large-scale annotated images. It integrates the deep multimodal feature generation, deep cross-modal correlation learning and similarity search optimization through mining all the beneficial multimodal information sources in sketches and images,...
With the development of the economic and the popularity of smartphones, location-based service is receiving more and more attention. It can be used inside a building where GPS signals are often unavailable. Because of its low deployment cost, vision-based indoor localization is becoming popular in the complicated indoor environment. However, in order to increase the accuracy of indoor localization,...
This paper investigates the usability of Halftoning-based Block Truncation Coding (HBTC) feature for image retrieval. It assumes that all images in database are stored in scrambled/encrypted format. Firstly, an image feature descriptor is derived from the scrambled/encrypted image. This image feature is subsequently converted into the binary representation to achieve fast similarity measurement. The...
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