Optimized Cartesian KK-Means

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named Optimized Cartesian $K$-Means (OCKM), to better encode the data points for more accurate approximate nearest neighbor search. In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life datasets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.Comment: to appear in IEEE TKDE, accepted in Apr. 201

Learning to Navigate the Energy Landscape

In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimization of the reconstruction error which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization. To make the RGB camera relocalization problem particularly challenging, we introduce a new dataset of 3D environments which are significantly larger than those found in other publicly-available datasets. Our experiments reveal that the proposed method is able to achieve state-of-the-art camera relocalization results. We also demonstrate the generalizability of our approach on Hand Pose Estimation and Image Retrieval tasks

A novel method for SIFT features matching based on feature dimension matching degree

We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature descriptor vector, thus obtaining the number of dimensions matching between feature points and feature dimension matching degree. Then we use the feature dimension matching degree to obtain the suspect matching feature points. Finally we use the Euclidean distance to eliminate the mismatching feature points to obtain accurate matching feature point pairs. The method is essentially a high-dimensional feature vector matching method based on local feature vector element matching. Experimental results show that the new algorithm can guarantee the number of matching SIFT feature points and their matching accuracy and that its running time is similar to that of HKMT, RKDT and LSH algorithm

NV-tree : a scalable disk-based high-dimensional index

This thesis presents the NV-tree (Nearest Vector tree), which addresses thespecific problem of efficiently and effectively finding the approximatek-nearest neighbors within large collections of high-dimensional data points.The NV-tree is a very compact index, as only six bytes are kept in the in-dex for each high-dimensional descriptor. It thus scales extremely well whenindexing large collections of high-dimensional descriptors. The NV-tree ef-ficiently produces results of good quality, even at such a large scale that theindices can no longer be kept entirely in main memory. We demonstrate thiswith extensive experiments presenting results from various collection sizesfrom 36 million up to nearly 30 billion SIFT (Scale Invariant Feature Trans-form) descriptors.We also study the conditions under which a nearest neighbour search pro-vides meaningful results. Following this analysis we compare the NV-tree toLSH (Locality Sensitive Hashing), the most popular method for -distancesearch, showing that the NV-tree outperforms LSH when it comes to theproblem of nearest neighbour retrieval. Beyond this analysis we also dis-cuss how the NV-tree index can be used in practise in industrial applicationsand address two frequently overlooked requirements: dynamicity—the abil-ity to cope with on-line insertions of new high-dimensional items into theindexed collection—and durability—the ability to recover from crashes andavoid losing the indexed data if a failure occurs. As far as we know, no othernearest neighbor algorithm published so far is able to cope with all threerequirements: scale, dynamicity and durability.Í þessari ritgerð setjum við fram vísinn NV-tré (e. NV-tree) sem lausn áákveðnu afmörkuðu vandamáli: að finna, á hraðvirkan og markvirkan hátt,nálgun áknæstu nágrönnum í stóru safni margvíðra gagnapunkta. NV-tréðer mjög fyrirferðarlítill vísir, þar sem aðeins sex bæti eru geymd fyrir hvernmargvíðan lýsivektor (e. descriptor). NV-tréð skalast því mjög vel þegar þvíer beitt á stór söfn margvíðra lýsivektora. NV-tréð skilar góðum niðurstöðumá skömmum tíma, jafnvel þegar vísarnir komast ekki fyrir í minni. Viðsýnum fram á þetta með niðurstöðum tilrauna á söfnum sem innihalda frá 36milljónum upp í nærri 30 milljarða SIFT (e. Scale Invariant Feature Trans-form) lýsivektora. Við rannsökum einnig þau skilyrði sem þurfa að vera fyrir hendi til að leitað næstu nágrönnum skili merkingarbærum niðurstöðum. Í framhaldi afþeirri greiningu berum við NV-tréð saman við LSH (e. Locality SensitiveHashing), sem er vinsælasta aðferðin fyrir -fjarlægðarleit, og sýnum að NV-tréð er mun hraðvirkara en LSH. Til viðbótar við þessa greiningu ræðumvið hagnýtingu NV-trésins í iðnaði og uppfyllum tvær þarfir sem oft er litiðframhjá: breytileika (e. dynamicity)—getu til að höndla í rauntíma viðbæ-tur við lýsingasafnið—og varanleika (e. durability)—getu til að endurheimtavísinn og forðast gagnatap ef um tölvubilun er að ræða. Að því er við bestvitum, uppfyllir enginn annar þekktur vísir allar þessar þrjár þarfir: skalan-leika, breytileika og varanleika

Boosting multi-kernel Locality-Sensitive Hashing for scalable image retrieval

Ministry of Education, Singapore under its Academic Research Funding Tier