In digital image processing, feature extraction occupies a very important position, which is related to the effect of image classification or recognition. Experimental results on various descriptors and tasks demonstrate that the proposed ProLFA is considerably superior over currently available alternatives about feature aggregation. Furthermore, ProLFA is also provided with a powerful generalization ability to deal flexibly with the semi-supervised and fully supervised scenarios in local feature aggregation. Meanwhile, to strengthen the discriminability of the aggregated representation, we rationally enforce the domain-invariant projection of bundled descriptors along a task-specific direction. It is capable of producing compact yet interpretable representations by selecting representative prototypes from numerous descriptors, under relaxed exclusivity constraint. In this paper, we propose a generic formulation to provide a systematical solution (named ProLFA) to aggregate local descriptors. Existing feature aggregation (FA) approaches, including Bag of Words and Fisher Vectors, usually fail to capture the desired information due to their pipeline mode. Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals.

We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We are interested in the encoding of local descriptors of an image (e.g.

Super vectorizer 2 car