In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for 'the same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21 on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.
Traditionally, image-based search methods drew their inspiration from textual retrieval systems @cite_25 . By using @math -means clustering method in the space of local feature descriptors such as SIFT @cite_19 , they are able to mimic textual word entities with the so-called visual words . Once the mapping from image salient keypoints to visually representative words was established, typical textual retrieval methods such as Bag-of-Words @cite_5 could be used. Video Google @cite_3 was one of the first visual search engines that relied on this concept. Several extensions of this concept were proposed, e.g. spatial verification @cite_9 that checks for geometrical correctness of initial query or fine-grained image search @cite_33 that accounts for semantic attributes of visual words.
@cite_33 @cite_9 @cite_3 @cite_19 @cite_5 @cite_25
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In state-of-the-art image retrieval systems, an image is represented by a bag of visual words obtained by quantizing high-dimensional local image descriptors, and scalable schemes inspired by text retrieval are then applied for large scale image indexing and retrieval. Bag-of-words representations, however: 1) reduce the discriminative power of image features due to feature quantization; and 2) ignore geometric relationships among visual words. Exploiting such geometric constraints, by estimating a 2D affine transformation between a query image and each candidate image, has been shown to greatly improve retrieval precision but at high computational cost. In this paper we present a novel scheme where image features are bundled into local groups. Each group of bundled features becomes much more discriminative than a single feature, and within each group simple and robust geometric constraints can be efficiently enforced. Experiments in Web image search, with a database of more than one million images, show that our scheme achieves a 49 improvement in average precision over the baseline bag-of-words approach. Retrieval performance is comparable to existing full geometric verification approaches while being much less computationally expensive. When combined with full geometric verification we achieve a 77 precision improvement over the baseline bag-of-words approach, and a 24 improvement over full geometric verification alone. The state-of-the-art image retrieval approaches represent images with a high dimensional vector of visual words by quantizing local features, such as SIFT, in the descriptor space. The geometric clues among visual words in an image is usually ignored or exploited for full geometric verification, which is computationally expensive. In this paper, we focus on partial-duplicate web image retrieval, and propose a novel scheme, spatial coding, to encode the spatial relationships among local features in an image. Our spatial coding is both efficient and effective to discover false matches of local features between images, and can greatly improve retrieval performance. Experiments in partial-duplicate web image search, using a database of one million images, reveal that our approach achieves a 53 improvement in mean average precision and 46 reduction in time cost over the baseline bag-of-words approach. This paper presents a Bag of Visual Words (BoVW) based approach to retrieve similar word images from a large database, efficiently and accurately. We show that a text retrieval system can be adapted to build a word image retrieval solution. This helps in achieving scalability. We demonstrate the method on more than 1 Million word images with a sub-second retrieval time. We validate the method on four Indian languages, and report a mean average precision of more than 0.75. We represent the word images as histogram of visual words present in the image. Visual words are quantized representation of local regions, and for this work, SIFT descriptors at interest points are used as feature vectors. To address the lack of spatial structure in the BoVW representation, we re-rank the retrieved list. This significantly improves the performance. The objective of this paper is large scale object instance retrieval, given a query image. A starting point of such systems is feature detection and description, for example using SIFT. The focus of this paper, however, is towards very large scale retrieval where, due to storage requirements, very compact image descriptors are required and no information about the original SIFT descriptors can be accessed directly at run time. We start from VLAD, the state-of-the art compact descriptor introduced by for this purpose, and make three novel contributions: first, we show that a simple change to the normalization method significantly improves retrieval performance, second, we show that vocabulary adaptation can substantially alleviate problems caused when images are added to the dataset after initial vocabulary learning. These two methods set a new state-of-the-art over all benchmarks investigated here for both mid-dimensional (20k-D to 30k-D) and small (128-D) descriptors. Our third contribution is a multiple spatial VLAD representation, MultiVLAD, that allows the retrieval and localization of objects that only extend over a small part of an image (again without requiring use of the original image SIFT descriptors).