Direct Image-Centric Detection (DICDM)

• Direct Image-Centric Detection Model (DICDM) is a paradigm that bypasses manual feature extraction by processing raw or lightly preprocessed image data.
• It leverages inherent spatial, textural, and statistical cues from pixels to drive prediction in applications like image authenticity and agricultural disease detection.
• While offering a simplified pipeline and rapid deployment, DICDM can underperform compared to feature-based models in high-precision classification tasks.

A Direct Image-Centric Detection Model (DICDM) is a detection paradigm in which decisions or predictions are performed directly on the raw (or lightly preprocessed) image data, forgoing explicit hand-crafted or learned feature extraction stages and minimizing or eliminating the manual engineering of intermediate representations. Image-centric detection emphasizes the exploitation of spatial, textural, or statistical cues present in the pixel values, allowing learning or algorithmic logic to operate on full image or localized pixel information. This approach is distinct from feature-based pipelines, which rely on explicit extraction of salient features prior to classification or detection. DICDM is utilized both in classical supervised object detection and in specific applications such as image authenticity verification and agricultural disease classification.

1. Foundational Concepts and Definitions

Direct Image-Centric Detection Models are characterized by the absence of engineered feature extraction and selection stages. In a prototypical DICDM, images—potentially after basic normalization or segmentation—are provided as input to a classifier (such as an artificial neural network) or to an algorithm that directly operates upon pixel statistics or spatial configurations. The detection or classification is thus “image-centric,” relying on the information present in the image domain without abstraction into intermediate feature sets.

A defining feature of DICDM is the direct use of spatial or statistical relationships at the pixel level, whether through learned neural network parameters (as in the case of Extreme Learning Machines or directly supervised convolutional nets) (2507.02322), or through localized statistical analysis (as in the detection of generative manipulations via Bayer demosaicing patterns and local variance signatures) (2310.16684).

2. Methodological Frameworks and Example Pipelines

DICDM encompasses diverse methodological instantiations across application areas. Two principal workflows are as follows:

A. Supervised Classification Pipeline (e.g., agricultural disease recognition)

• Image preprocessing and segmentation: Input images are standardized in size (e.g., resized to 256×256), followed by data augmentation such as flipping, rotation, and scaling transformations applied via equations of the form

$f(x, y) = f(W - x, y)$

where $W$ is the image width for horizontal flipping, and counterpart transformations for rotation/scaling. Normalization (e.g., min–max scaling) and contrast enhancement are applied.

• Segmentation: Conversion to an appropriate color space (e.g., Lab), with disease-affected regions identified using thresholding on a* channel values:

$$I_{binary}(x,y) = \begin{cases} 1, & a^*(x,y) > T \ 0, & a^*(x,y) \leq T \end{cases}$$

• Direct input to classifier: The resulting image pixel array (of size $M \times N$) is vectorized and entered into an artificial neural network—such as an Extreme Learning Machine with a specified number of hidden neurons. Output is computed as

$O = H \times \beta$

where $H$ is the hidden layer activation matrix and $\beta$ is the matrix of output weights. Softmax is typically applied for multi-class classification. No explicit feature extraction, dimensionality reduction, or selection is involved in this direct path (2507.02322).

B. Local Statistics Pipeline (e.g., generative image detection):

• Localized filtering and derivatives: Images undergo high-pass filtering, with application of $2\times2$ convolution kernels to compute local gradients along main and anti-diagonals:

$$\text{Main diagonal}: \begin{bmatrix} 1 & 0 \ 0 & -1 \end{bmatrix} \quad \text{Anti-diagonal}: \begin{bmatrix} 0 & 1 \ -1 & 0 \end{bmatrix}$$

• Block-wise statistics: The image is divided into $10 \times 10$ pixel blocks. Within each block, variances along diagonal axes are computed and summed for each channel, yielding a spatial map of local variance.
• Inter-channel correlation (for forensic detection): Pearson correlation coefficients are computed for the block-reduced variance images across color channels to exploit sensor-specific statistical traces (such as the Bayer pattern) (2310.16684).

3. Applications Across Domains

A. Digital Forensics and Image Authenticity

In the context of distinguishing digitally acquired images from those synthesized by diffusion models, DICDM utilizes local pixel statistics and color channel correlations to detect demosaicing artifacts and the lack of Bayer pattern signatures. The model's performance is robust against resizing and JPEG compression perturbations, consistently maintaining high AUC scores (2310.16684). Key technical tools include diagonal/anti-diagonal gradient operators, variance calculation formulas, and frequency-domain analysis to identify periodicity introduced by demosaicing.

B. Agricultural Disease Recognition

For rice leaf disease detection and classification, DICDM provides a pipeline wherein preprocessed and segmented leaf images are fed, in raw or pixel-based form, directly to a classifier. Performance is evaluated using sensitivity, specificity, precision, F-measure, and accuracy. In these studies, DICDM achieves moderate accuracy (approximately 75%), which is typically lower than feature-based detection models (FADM) that utilize deliberately engineered features for classification (achieving up to 98.99% accuracy) (2507.02322). The DICDM approach simplifies data ingestion and reduces pipeline complexity, but is susceptible to lower discriminative power when task-relevant features are not sufficiently learnable from raw pixels alone.

C. Object Detection and Sensor Fusion

Some DICDM approaches incorporate multi-modal detection, remaining image-centric while integrating auxiliary sensor information. For example, in long-range 3D detection with SpotNet, high-resolution image features serve as the core input, with sparse LiDAR data projected into image space and concatenated as additional channels. Detection is performed in the image domain, with back-projection into physical coordinates via LiDAR anchoring. Supervision is applied only at spatial locations with valid LiDAR returns, and both 2D and 3D detection objectives are simultaneously optimized (2405.15843). This structure allows constant-time inference with respect to range and enables seamless transfer across image resolutions without re-training.

4. Comparison with Feature-Based Detection and Hybrid Models

DICDM is directly contrasted with feature analysis detection models (FADM) in rigorous empirical studies. FADM pipelines comprise three main stages: hand-crafted feature extraction (e.g., texture, GLCM, FFT, DWT), dimensionality reduction (e.g., PCA, KPCA, autoencoders), and discriminative feature selection algorithms (Anova F-measure, Chi-square, Random Forest). In rice leaf disease classification, FADM achieves nearly 99% accuracy, outperforming DICDM by a wide margin in sensitivity, specificity, precision, and F-measure (2507.02322). The results indicate that direct pixel-based classification in DICDM is often outperformed by models that exploit structured domain knowledge through feature engineering; however, DICDM presents significantly reduced labor and pipeline complexity.

5. Empirical Performance and Evaluation Metrics

Performance of DICDM is task-dependent and measured using standardized classification and detection metrics:

• In agricultural disease detection (2507.02322), DICDM achieves:
  • Sensitivity: 73%
  • Specificity: 77%
  • Precision: 65%
  • F-measure: 64%
  • Accuracy: $74.97\% \pm 0.8\%$ (across classes)
• For generative image detection (2310.16684), the AUC (Area Under Curve) for detecting synthesized images remains high across varying image sizes and under perturbations.
• In multi-modal 3D detection (2405.15843), DICDM-based architectures such as SpotNet demonstrate constant $O(1)$ scaling with respect to scene range and successful transfer from lower to higher image resolutions without retraining.

6. Advantages, Limitations, and Practical Considerations

Advantages:

• Drastically simplified implementation pipeline, as DICDM eliminates manual feature engineering and selection.
• Full utilization of image information, preserving potential subtle discriminative cues.
• Straightforward integration with neural architectures, especially for large annotated datasets and scenarios where features are difficult to intuitively engineer.

Limitations:

• High-dimensional input (e.g., 65,536 pixel values for a $256 \times 256$ image) may present computational challenges and risk overfitting or underfitting, especially in low-data regimes.
• Lower classification performance for specialized detection tasks compared to feature-engineered approaches, where explicit domain-relevant features drive discrimination.
• May include redundant or noisy information detrimental to classifier learning, unless mitigated by very large datasets or regularization.

These findings suggest that while DICDM is attractive for rapid deployment and end-to-end learning in settings with abundant annotated data, for domains demanding maximum predictive accuracy—such as high-precision agricultural disease monitoring—feature-based or hybrid detection models may remain preferable.

7. Significance and Directions in Research

Direct Image-Centric Detection Models represent a growing trend toward end-to-end, data-driven inference in image-based detection tasks, motivated by advances in neural architectures and increasing computational power. Their practical traction is significant in settings where feature engineering is cost-prohibitive, image statistics encode domain-unique signals (as in forensic authenticity analysis), or rapid pipeline simplicity is prioritized. However, in domains where handcrafted features encode essential discriminatory capacity—such as in complex or noisy backgrounds—explicit feature extraction retains considerable value.

A plausible implication is that future DICDM research will increasingly converge with learned feature extraction in deep neural networks, leading to hybrid architectures that blend the strengths of direct image analysis with hierarchical feature learning, exploiting domain knowledge when available while preserving the benefits of data-centric end-to-end optimization.