Unsupervised image hashing is a widely used technique for large-scale image retrieval. This technique maps an image to a finite length of binary codes without extensive human-annotated data for compact storage and effective semantic retrieval. This study proposes a novel deep unsupervised double-bit hashing method for image retrieval. This approach is based on the double-bit hashing method, which has been shown to better preserve the neighboring structure of binary codes than single-bit hashing. Traditional double-bit hashing methods require the entire dataset to be processed simultaneously to determine optimal thresholding values of binary feature encoding. In contrast, the proposed method trains the hashing layer in a minibatch manner, allowing for adaptive threshold learning through a gradient-based optimization strategy. Additionally, unlike most former methods, which only train the hashing networks on top of fixed pre-trained neural networks backbone. The proposed learning framework trains both hashing and backbone networks alternately asynchronously. This strategy enables the model to maximize the learning capability of the hashing and backbone networks. Furthermore, adopting the lightweight Vision Transformer (ViT) in the proposed method allows the model to capture both local and global relationships between multiple image views exemplar, which lead to better generalization, thus maximizing the retrieval performance of the model. Extensive experiments on CIFAR10, NUS-WIDE, and FLICKR25K datasets validate that the proposed method has superior retrieval quality and computational efficiency than state-of-the-art methods.

This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and a bitmap image, which are further processed using vector quantization (VQ) to generate the image feature descriptor. Herein two features are introduced, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), to measure the similarity between a query image and the target image in database. The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF can be utilized to measure the similarity between two images. As documented in the experimental result, the proposed indexing method outperforms the former block truncation coding based image indexing and the other existing image retrieval schemes with natural and textural data sets. Thus, the proposed EDBTC is not only examined with good capability for image compression but also offers an effective way to index images for the content-based image retrieval system.

This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.

This paper presents a new feature descriptor for vehicle verification. The object detection scheme generates the vehicle hypothesis (candidate) that requires subsequent confirmation in the vehicle verification stage with specific feature descriptors. In the procedure of vehicle verification, an image descriptor is generated from the statistical parameter of the curvelet-transformed (CT) subbands. The marginal distribution of CT output is a heavy-tailed bell-shaped function, which can be approximated as Gaussian, Laplace, and generalized Gaussian distribution (GGD) with high accuracy. The maximum likelihood estimation (MLE) produces the distribution parameters of each CT subband for the generation of the image feature descriptor. The classifier then assigns a class label for the vehicle hypothesis based on this descriptor information. As documented in the experimental results, this feature descriptor is effective and outperforms the existing methods in the vehicle verification tasks.

This paper presents a new approach to derive the image feature descriptor from the dot-diffused block truncation coding (DDBTC) compressed data stream. The image feature descriptor is simply constructed from two DDBTC representative color quantizers and its corresponding bitmap image. The color histogram feature (CHF) derived from two color quantizers represents the color distribution and image contrast, while the bit pattern feature (BPF) constructed from the bitmap image characterizes the image edges and textural information. The similarity between two images can be easily measured from their CHF and BPF values using a specific distance metric computation. Experimental results demonstrate the superiority of the proposed feature descriptor compared to the former existing schemes in image retrieval task under natural and textural images. The DDBTC method compresses an image efficiently, and at the same time, its corresponding compressed data stream can provide an effective feature descriptor for performing image retrieval and classification. Consequently, the proposed scheme can be considered as an effective candidate for real-time image retrieval applications.

This letter presents a new method to derive the image feature descriptor for vehicle verification. The effectiveness of the proposed feature descriptor is based on the nature of the Gabor filter magnitude that tends to obey the Gamma distribution. The statistical parameters of the Gabor magnitude are computed using the Maximum Likelihood Estimation (MLE), which is later utilized to construct the feature descriptor. Conventionally, the Gabor magnitude is simply modeled by using Gaussian distribution, and thus the image descriptor consists of mean, standard deviation, and skewness values of the Gabor filter magnitude. However, recent investigations found that the skewness parameter is not contributing towards class separation. Based on our observation, the Gamma distribution provides a better statistical fitting to represent the Gabor filter magnitude when compared to the Gaussian distribution. As documented in the experimental results, the proposed feature descriptor yields higher accuracy for vehicle verification when compared to the conventional schemes.

This paper presents a technique for Content-Based Image Retrieval (CBIR) by exploiting the low complexity advantage of the Ordered-Dither Block Truncation Coding (ODBTC) for generating image content descriptors. The two image features, namely Color Co-occurrence Feature (CCF) and Bit Pattern Features (BPF), are generated from ODBTC encoded data streams (without really performing an image compression or decoding process) to measure the similarity between two images. Experimental results show that the proposed method is superior to the Block Truncation Coding (BTC) image retrieval system and other former methods, and prove that the ODBTC scheme is not only suited for image compression for its simplicity, but also offers a conveniently way for image indexing in the content-based image retrieval system.