Unsupervised Local Feature Hashing for Image Similarity Search

The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global feature representations, which are susceptible to image variations such as viewpoint changes and background cluttering. Traditional global representations gather local features directly to output a single vector without the analysis of the intrinsic geometric property of local features. In this paper, we propose a novel unsupervised hashing method called unsupervised bilinear local hashing (UBLH) for projecting local feature descriptors from a high-dimensional feature space to a lower-dimensional Hamming space via compact bilinear projections rather than a single large projection matrix. UBLH takes the matrix expression of local features as input and preserves the feature-to-feature and image-to-image structures of local features simultaneously. Experimental results on challenging data sets including Caltech-256, SUN397, and Flickr 1M demonstrate the superiority of UBLH compared with state-of-the-art hashing methods

Retrieval of Images Using Color, Shape and Texture Features Based on Content

The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications

Information Is Selection-A Review of Basics Shows Substantial Potential for Improvement of Digital Information Representation

Any piece of information is a selection from a set of possibilities. In this paper, this set is called a "domain". Digital information consists of number sequences, which are selections from a domain. At present, these number sequences are defined contextually in a very variable way, which impairs their comparability. Therefore, global uniformly defined "domain vectors" (DVs), with a structure containing a "Uniform Locator" ("UL"), referred to as "UL plus number sequence", are proposed. The "UL" is an efficient global pointer to the uniform online definition of the subsequent number sequence. DVs are globally defined, identified, comparable, and searchable by criteria which users can define online. In medicine, for example, patients, doctors, and medical specialists can define DVs online and can, therefore, form global criteria which are important for certain diagnoses. This allows for the immediate generation of precise diagnostic specific statistics of "similar medical cases", in order to discern the best therapy. The introduction of a compact DV data structure may substantially improve the digital representation of medical information

Hetero-manifold Regularisation for Cross-modal Hashing

Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks

Dynamic match kernel with deep convolutional features for image retrieval

For image retrieval methods based on bag of visual words, much attention has been paid to enhancing the discriminative powers of the local features. Although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (CNN). Such images should not be considered as relevant pairs. To tackle this problem, we propose to construct a dynamic match kernel by adaptively calculating the matching thresholds between query and candidate images based on the pairwise distance among deep CNN features. In contrast to the typical static match kernel which is independent to the global appearance of retrieved images, the dynamic one leverages the semantical similarity as a constraint for determining the matches. Accordingly, we propose a semantic-constrained retrieval framework by incorporating the dynamic match kernel, which focuses on matched patches between relevant images and filters out the ones for irrelevant pairs. Furthermore, we demonstrate that the proposed kernel complements recent methods, such as hamming embedding, multiple assignment, local descriptors aggregation, and graph-based re-ranking, while it outperforms the static one under various settings on off-the-shelf evaluation metrics. We also propose to evaluate the matched patches both quantitatively and qualitatively. Extensive experiments on five benchmark data sets and large-scale distractors validate the merits of the proposed method against the state-of-the-art methods for image retrieval

Sequential compact code learning for unsupervised image hashing

Effective hashing for large-scale image databases is a popular research area, attracting much attention in computer vision and visual information retrieval. Several recent methods attempt to learn either graph embedding or semantic coding for fast and accurate applications. In this paper, a novel unsupervised framework, termed evolutionary compact embedding (ECE), is introduced to automatically learn the task-specific binary hash codes. It can be regarded as an optimization algorithm that combines the genetic programming (GP) and a boosting trick. In our architecture, each bit of ECE is iteratively computed using a weak binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimization by embedding high-dimensional data points into a similarity-preserved Hamming space with a low dimension. We systematically evaluate ECE on two data sets, SIFT 1M and GIST 1M, showing the effectiveness and the accuracy of our method for a large-scale similarity search