Pattern recognition has emerged as an effective measure for automated system. I n such an approach the coding is carried out over a set of images and result are retrieved based on the best match approach. Where image features are taken as the representative feature for image coding, the introduced artifacts are the major effects at the coding level. In this work a shape oriented coding based on...

Fractal image coding suffers from its long encoding time. Block classification is often used to speed up coding processing, which uses some mechanism of classification when coding an image and only does match job for domain and range blocks with some similar features. D. Saupe’s classification algorithm of nearest neighbor search in feature space seems to have the best rate-distortion property ...

This paper takes manifold learning and regression simultaneously into account to perform unsupervised spectral feature selection. We first extract the bases of the data, and then represent the data sparsely using the extracted bases by proposing a novel joint graph sparse coding model, JGSC for short. We design a new algorithm TOSC to compute the resulting objective function of JGSC, and then t...

Many state-of-the-art methods in object recognition extract features from an image and encode them, followed by a pooling step and classification. Within this processing pipeline, often the encoding step is the bottleneck, for both computational efficiency and performance. We present a novel assignment-based encoding formulation. It allows for the fusion of assignment-based encoding and sparse ...

A feature-oriented generic progressive lossless mesh coder (FOLProM) is proposed to encode triangular meshes with arbitrarily complex geometry and topology. In this work, a sequence of levels of detail (LODs) are generated through iterative vertex set split and bounding volume subdivision. The incremental geometry and connectivity updates associated with each vertex set split and/or bounding vo...

In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial poo...

Hierarchical feed-forward networks have been successfully applied in object recognition. At each level of the hierarchy, features are extracted and encoded, followed by a pooling step. Within this processing pipeline, the common trend is to learn the feature coding templates, often referred as codebook entries, filters, or over-complete basis. Recently, an approach that apparently does not use ...

We investigate transform coding to efficiently store and transmit SIFT and SURF image descriptors. We show that image and feature matching algorithms are robust to significantly compressed features. We achieve nearperfect image matching and retrieval for both SIFT and SURF using ∼2 bits/dimension. When applied to SIFT and SURF, this provides a 16× compression relative to conventional floating p...

In mobile visual search applications, an image-based query is typically sent from a mobile client to the server. Because of the bit-rate limitations, the query should be as small as possible. When performing image-based retrieval with local features, there are two types of information: the descriptors of the image features and the locations of the image features within the image. Location infor...

Underwater acoustic target recognition (UATR) technology based on deep learning and automatic encoding has become an important research direction in the underwater field recent years. However, existing methods do not have favourable self-adaptability for different data because of complex changeable environment, which easily leads to unsatisfactory effect. The concept contrastive is introduced i...